An Efficient Approach for Pixel Decomposition to Increase the Spatial Resolution of Land Surface Temperature Images from MODIS Thermal Infrared Band Data

PubMed Central

Wang, Fei; Qin, Zhihao; Li, Wenjuan; Song, Caiying; Karnieli, Arnon; Zhao, Shuhe

2015-01-01

Land surface temperature (LST) images retrieved from the thermal infrared (TIR) band data of Moderate Resolution Imaging Spectroradiometer (MODIS) have much lower spatial resolution than the MODIS visible and near-infrared (VNIR) band data. The coarse pixel scale of MODIS LST images (1000 m under nadir) have limited their capability in applying to many studies required high spatial resolution in comparison of the MODIS VNIR band data with pixel scale of 250–500 m. In this paper we intend to develop an efficient approach for pixel decomposition to increase the spatial resolution of MODIS LST image using the VNIR band data as assistance. The unique feature of this approach is to maintain the thermal radiance of parent pixels in the MODIS LST image unchanged after they are decomposed into the sub-pixels in the resulted image. There are two important steps in the decomposition: initial temperature estimation and final temperature determination. Therefore the approach can be termed double-step pixel decomposition (DSPD). Both steps involve a series of procedures to achieve the final result of decomposed LST image, including classification of the surface patterns, establishment of LST change with normalized difference of vegetation index (NDVI) and building index (NDBI), reversion of LST into thermal radiance through Planck equation, and computation of weights for the sub-pixels of the resulted image. Since the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) with much higher spatial resolution than MODIS data was on-board the same platform (Terra) as MODIS for Earth observation, an experiment had been done in the study to validate the accuracy and efficiency of our approach for pixel decomposition. The ASTER LST image was used as the reference to compare with the decomposed LST image. The result showed that the spatial distribution of the decomposed LST image was very similar to that of the ASTER LST image with a root mean square error (RMSE) of 2.7 K for entire image. Comparison with the evaluation DisTrad (E-DisTrad) and re-sampling methods for pixel decomposition also indicate that our DSPD has the lowest RMSE in all cases, including urban region, water bodies, and natural terrain. The obvious increase in spatial resolution remarkably uplifts the capability of the coarse MODIS LST images in highlighting the details of LST variation. Therefore it can be concluded that, in spite of complicated procedures, the proposed DSPD approach provides an alternative to improve the spatial resolution of MODIS LST image hence expand its applicability to the real world. PMID:25609048

Does the Data Resolution/origin Matter? Satellite, Airborne and Uav Imagery to Tackle Plant Invasions

NASA Astrophysics Data System (ADS)

Müllerová, Jana; Brůna, Josef; Dvořák, Petr; Bartaloš, Tomáš; Vítková, Michaela

2016-06-01

Invasive plant species represent a serious threat to biodiversity and landscape as well as human health and socio-economy. To successfully fight plant invasions, new methods enabling fast and efficient monitoring, such as remote sensing, are needed. In an ongoing project, optical remote sensing (RS) data of different origin (satellite, aerial and UAV), spectral (panchromatic, multispectral and color), spatial (very high to medium) and temporal resolution, and various technical approaches (object-, pixelbased and combined) are tested to choose the best strategies for monitoring of four invasive plant species (giant hogweed, black locust, tree of heaven and exotic knotweeds). In our study, we address trade-offs between spectral, spatial and temporal resolutions required for balance between the precision of detection and economic feasibility. For the best results, it is necessary to choose best combination of spatial and spectral resolution and phenological stage of the plant in focus. For species forming distinct inflorescences such as giant hogweed iterative semi-automated object-oriented approach was successfully applied even for low spectral resolution data (if pixel size was sufficient) whereas for lower spatial resolution satellite imagery or less distinct species with complicated architecture such as knotweed, combination of pixel and object based approaches was used. High accuracies achieved for very high resolution data indicate the possible application of described methodology for monitoring invasions and their long-term dynamics elsewhere, making management measures comparably precise, fast and efficient. This knowledge serves as a basis for prediction, monitoring and prioritization of management targets.

Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing.

PubMed

Midekisa, Alemayehu; Holl, Felix; Savory, David J; Andrade-Pacheco, Ricardo; Gething, Peter W; Bennett, Adam; Sturrock, Hugh J W

2017-01-01

Quantifying and monitoring the spatial and temporal dynamics of the global land cover is critical for better understanding many of the Earth's land surface processes. However, the lack of regularly updated, continental-scale, and high spatial resolution (30 m) land cover data limit our ability to better understand the spatial extent and the temporal dynamics of land surface changes. Despite the free availability of high spatial resolution Landsat satellite data, continental-scale land cover mapping using high resolution Landsat satellite data was not feasible until now due to the need for high-performance computing to store, process, and analyze this large volume of high resolution satellite data. In this study, we present an approach to quantify continental land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources.

Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing

PubMed Central

Holl, Felix; Savory, David J.; Andrade-Pacheco, Ricardo; Gething, Peter W.; Bennett, Adam; Sturrock, Hugh J. W.

2017-01-01

Quantifying and monitoring the spatial and temporal dynamics of the global land cover is critical for better understanding many of the Earth’s land surface processes. However, the lack of regularly updated, continental-scale, and high spatial resolution (30 m) land cover data limit our ability to better understand the spatial extent and the temporal dynamics of land surface changes. Despite the free availability of high spatial resolution Landsat satellite data, continental-scale land cover mapping using high resolution Landsat satellite data was not feasible until now due to the need for high-performance computing to store, process, and analyze this large volume of high resolution satellite data. In this study, we present an approach to quantify continental land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources. PMID:28953943

Investigation of spatial resolution and temporal performance of SAPHIRE (scintillator avalanche photoconductor with high resolution emitter readout) with integrated electrostatic focusing

NASA Astrophysics Data System (ADS)

Scaduto, David A.; Lubinsky, Anthony R.; Rowlands, John A.; Kenmotsu, Hidenori; Nishimoto, Norihito; Nishino, Takeshi; Tanioka, Kenkichi; Zhao, Wei

2014-03-01

We have previously proposed SAPHIRE (scintillator avalanche photoconductor with high resolution emitter readout), a novel detector concept with potentially superior spatial resolution and low-dose performance compared with existing flat-panel imagers. The detector comprises a scintillator that is optically coupled to an amorphous selenium photoconductor operated with avalanche gain, known as high-gain avalanche rushing photoconductor (HARP). High resolution electron beam readout is achieved using a field emitter array (FEA). This combination of avalanche gain, allowing for very low-dose imaging, and electron emitter readout, providing high spatial resolution, offers potentially superior image quality compared with existing flat-panel imagers, with specific applications to fluoroscopy and breast imaging. Through the present collaboration, a prototype HARP sensor with integrated electrostatic focusing and nano- Spindt FEA readout technology has been fabricated. The integrated electron-optic focusing approach is more suitable for fabricating large-area detectors. We investigate the dependence of spatial resolution on sensor structure and operating conditions, and compare the performance of electrostatic focusing with previous technologies. Our results show a clear dependence of spatial resolution on electrostatic focusing potential, with performance approaching that of the previous design with external mesh-electrode. Further, temporal performance (lag) of the detector is evaluated and the results show that the integrated electrostatic focusing design exhibits comparable or better performance compared with the mesh-electrode design. This study represents the first technical evaluation and characterization of the SAPHIRE concept with integrated electrostatic focusing.

Estimation of Orbital Neutron Detector Spatial Resolution by Systematic Shifting of Differential Topographic Masks

NASA Technical Reports Server (NTRS)

McClanahan, T. P.; Mitrofanov, I. G.; Boynton, W. V.; Chin, G.; Livengood, T.; Starr, R. D.; Evans, L. G.; Mazarico, E.; Smith, D. E.

2012-01-01

We present a method and preliminary results related to determining the spatial resolution of orbital neutron detectors using epithermal maps and differential topographic masks. Our technique is similar to coded aperture imaging methods for optimizing photonic signals in telescopes [I]. In that approach photon masks with known spatial patterns in a telescope aperature are used to systematically restrict incoming photons which minimizes interference and enhances photon signal to noise. Three orbital neutron detector systems with different stated spatial resolutions are evaluated. The differing spatial resolutions arise due different orbital altitudes and the use of neutron collimation techniques. 1) The uncollimated Lunar Prospector Neutron Spectrometer (LPNS) system has spatial resolution of 45km FWHM from approx. 30km altitude mission phase [2]. The Lunar Rennaissance Orbiter (LRO) Lunar Exploration Neutron Detector (LEND) with two detectors at 50km altitude evaluated here: 2) the collimated 10km FWHM spatial resolution detector CSETN and 3) LEND's collimated Sensor for Epithermal Neutrons (SETN). Thus providing two orbital altitudes to study factors of: uncollimated vs collimated and two average altitudes for their effect on fields-of-view.

Chromatic and Achromatic Spatial Resolution of Local Field Potentials in Awake Cortex.

PubMed

Jansen, Michael; Li, Xiaobing; Lashgari, Reza; Kremkow, Jens; Bereshpolova, Yulia; Swadlow, Harvey A; Zaidi, Qasim; Alonso, Jose-Manuel

2015-10-01

Local field potentials (LFPs) have become an important measure of neuronal population activity in the brain and could provide robust signals to guide the implant of visual cortical prosthesis in the future. However, it remains unclear whether LFPs can detect weak cortical responses (e.g., cortical responses to equiluminant color) and whether they have enough visual spatial resolution to distinguish different chromatic and achromatic stimulus patterns. By recording from awake behaving macaques in primary visual cortex, here we demonstrate that LFPs respond robustly to pure chromatic stimuli and exhibit ∼2.5 times lower spatial resolution for chromatic than achromatic stimulus patterns, a value that resembles the ratio of achromatic/chromatic resolution measured with psychophysical experiments in humans. We also show that, although the spatial resolution of LFP decays with visual eccentricity as is also the case for single neurons, LFPs have higher spatial resolution and show weaker response suppression to low spatial frequencies than spiking multiunit activity. These results indicate that LFP recordings are an excellent approach to measure spatial resolution from local populations of neurons in visual cortex including those responsive to color. © The Author 2014. Published by Oxford University Press.

Scalability of grid- and subbasin-based land surface modeling approaches for hydrologic simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tesfa, Teklu K.; Ruby Leung, L.; Huang, Maoyi

2014-03-27

This paper investigates the relative merits of grid- and subbasin-based land surface modeling approaches for hydrologic simulations, with a focus on their scalability (i.e., abilities to perform consistently across a range of spatial resolutions) in simulating runoff generation. Simulations produced by the grid- and subbasin-based configurations of the Community Land Model (CLM) are compared at four spatial resolutions (0.125o, 0.25o, 0.5o and 1o) over the topographically diverse region of the U.S. Pacific Northwest. Using the 0.125o resolution simulation as the “reference”, statistical skill metrics are calculated and compared across simulations at 0.25o, 0.5o and 1o spatial resolutions of each modelingmore » approach at basin and topographic region levels. Results suggest significant scalability advantage for the subbasin-based approach compared to the grid-based approach for runoff generation. Basin level annual average relative errors of surface runoff at 0.25o, 0.5o, and 1o compared to 0.125o are 3%, 4%, and 6% for the subbasin-based configuration and 4%, 7%, and 11% for the grid-based configuration, respectively. The scalability advantages of the subbasin-based approach are more pronounced during winter/spring and over mountainous regions. The source of runoff scalability is found to be related to the scalability of major meteorological and land surface parameters of runoff generation. More specifically, the subbasin-based approach is more consistent across spatial scales than the grid-based approach in snowfall/rainfall partitioning, which is related to air temperature and surface elevation. Scalability of a topographic parameter used in the runoff parameterization also contributes to improved scalability of the rain driven saturated surface runoff component, particularly during winter. Hence this study demonstrates the importance of spatial structure for multi-scale modeling of hydrological processes, with implications to surface heat fluxes in coupled land-atmosphere modeling.« less

Area-to-point regression kriging for pan-sharpening

NASA Astrophysics Data System (ADS)

Wang, Qunming; Shi, Wenzhong; Atkinson, Peter M.

2016-04-01

Pan-sharpening is a technique to combine the fine spatial resolution panchromatic (PAN) band with the coarse spatial resolution multispectral bands of the same satellite to create a fine spatial resolution multispectral image. In this paper, area-to-point regression kriging (ATPRK) is proposed for pan-sharpening. ATPRK considers the PAN band as the covariate. Moreover, ATPRK is extended with a local approach, called adaptive ATPRK (AATPRK), which fits a regression model using a local, non-stationary scheme such that the regression coefficients change across the image. The two geostatistical approaches, ATPRK and AATPRK, were compared to the 13 state-of-the-art pan-sharpening approaches summarized in Vivone et al. (2015) in experiments on three separate datasets. ATPRK and AATPRK produced more accurate pan-sharpened images than the 13 benchmark algorithms in all three experiments. Unlike the benchmark algorithms, the two geostatistical solutions precisely preserved the spectral properties of the original coarse data. Furthermore, ATPRK can be enhanced by a local scheme in AATRPK, in cases where the residuals from a global regression model are such that their spatial character varies locally.

Photoacoustic tomography guided diffuse optical tomography for small-animal model

NASA Astrophysics Data System (ADS)

Wang, Yihan; Gao, Feng; Wan, Wenbo; Zhang, Yan; Li, Jiao

2015-03-01

Diffuse optical tomography (DOT) is a biomedical imaging technology for noninvasive visualization of spatial variation about the optical properties of tissue, which can be applied to in vivo small-animal disease model. However, traditional DOT suffers low spatial resolution due to tissue scattering. To overcome this intrinsic shortcoming, multi-modal approaches that incorporate DOT with other imaging techniques have been intensively investigated, where a priori information provided by the other modalities is normally used to reasonably regularize the inverse problem of DOT. Nevertheless, these approaches usually consider the anatomical structure, which is different from the optical structure. Photoacoustic tomography (PAT) is an emerging imaging modality that is particularly useful for visualizing lightabsorbing structures embedded in soft tissue with higher spatial resolution compared with pure optical imaging. Thus, we present a PAT-guided DOT approach, to obtain the location a priori information of optical structure provided by PAT first, and then guide DOT to reconstruct the optical parameters quantitatively. The results of reconstruction of phantom experiments demonstrate that both quantification and spatial resolution of DOT could be highly improved by the regularization of feasible-region information provided by PAT.

Simultaneous multiview capture and fusion improves spatial resolution in wide-field and light-sheet microscopy

PubMed Central

Wu, Yicong; Chandris, Panagiotis; Winter, Peter W.; Kim, Edward Y.; Jaumouillé, Valentin; Kumar, Abhishek; Guo, Min; Leung, Jacqueline M.; Smith, Corey; Rey-Suarez, Ivan; Liu, Huafeng; Waterman, Clare M.; Ramamurthi, Kumaran S.; La Riviere, Patrick J.; Shroff, Hari

2016-01-01

Most fluorescence microscopes are inefficient, collecting only a small fraction of the emitted light at any instant. Besides wasting valuable signal, this inefficiency also reduces spatial resolution and causes imaging volumes to exhibit significant resolution anisotropy. We describe microscopic and computational techniques that address these problems by simultaneously capturing and subsequently fusing and deconvolving multiple specimen views. Unlike previous methods that serially capture multiple views, our approach improves spatial resolution without introducing any additional illumination dose or compromising temporal resolution relative to conventional imaging. When applying our methods to single-view wide-field or dual-view light-sheet microscopy, we achieve a twofold improvement in volumetric resolution (~235 nm × 235 nm × 340 nm) as demonstrated on a variety of samples including microtubules in Toxoplasma gondii, SpoVM in sporulating Bacillus subtilis, and multiple protein distributions and organelles in eukaryotic cells. In every case, spatial resolution is improved with no drawback by harnessing previously unused fluorescence. PMID:27761486

Wavelength-independent constant period spin-echo modulated small angle neutron scattering

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sales, Morten, E-mail: lsp260@alumni.ku.dk; Plomp, Jeroen; Bouwman, Wim

2016-06-15

Spin-Echo Modulated Small Angle Neutron Scattering (SEMSANS) in Time-of-Flight (ToF) mode has been shown to be a promising technique for measuring (very) small angle neutron scattering (SANS) signals and performing quantitative Dark-Field Imaging (DFI), i.e., SANS with 2D spatial resolution. However, the wavelength dependence of the modulation period in the ToF spin-echo mode has so far limited the useful modulation periods to those resolvable with the limited spatial resolution of the detectors available. Here we present our results of an approach to keep the period of the induced modulation constant for the wavelengths utilised in ToF. This is achieved bymore » ramping the magnetic fields in the coils responsible for creating the spatially modulated beam in synchronisation with the neutron pulse, thus keeping the modulation period constant for all wavelengths. Such a setup enables the decoupling of the spatial detector resolution from the resolution of the modulation period by the use of slits or gratings in analogy to the approach in grating-based neutron DFI.« less

Efficient space-time sampling with pixel-wise coded exposure for high-speed imaging.

PubMed

Liu, Dengyu; Gu, Jinwei; Hitomi, Yasunobu; Gupta, Mohit; Mitsunaga, Tomoo; Nayar, Shree K

2014-02-01

Cameras face a fundamental trade-off between spatial and temporal resolution. Digital still cameras can capture images with high spatial resolution, but most high-speed video cameras have relatively low spatial resolution. It is hard to overcome this trade-off without incurring a significant increase in hardware costs. In this paper, we propose techniques for sampling, representing, and reconstructing the space-time volume to overcome this trade-off. Our approach has two important distinctions compared to previous works: 1) We achieve sparse representation of videos by learning an overcomplete dictionary on video patches, and 2) we adhere to practical hardware constraints on sampling schemes imposed by architectures of current image sensors, which means that our sampling function can be implemented on CMOS image sensors with modified control units in the future. We evaluate components of our approach, sampling function and sparse representation, by comparing them to several existing approaches. We also implement a prototype imaging system with pixel-wise coded exposure control using a liquid crystal on silicon device. System characteristics such as field of view and modulation transfer function are evaluated for our imaging system. Both simulations and experiments on a wide range of scenes show that our method can effectively reconstruct a video from a single coded image while maintaining high spatial resolution.

Impact of the spatial resolution of satellite remote sensing sensors in the quantification of total suspended sediment concentration: A case study in turbid waters of Northern Western Australia.

PubMed

Dorji, Passang; Fearns, Peter

2017-01-01

The impact of anthropogenic activities on coastal waters is a cause of concern because such activities add to the total suspended sediment (TSS) budget of the coastal waters, which have negative impacts on the coastal ecosystem. Satellite remote sensing provides a powerful tool in monitoring TSS concentration at high spatiotemporal resolution, but coastal managers should be mindful that the satellite-derived TSS concentrations are dependent on the satellite sensor's radiometric properties, atmospheric correction approaches, the spatial resolution and the limitations of specific TSS algorithms. In this study, we investigated the impact of different spatial resolutions of satellite sensor on the quantification of TSS concentration in coastal waters of northern Western Australia. We quantified the TSS product derived from MODerate resolution Imaging Spectroradiometer (MODIS)-Aqua, Landsat-8 Operational Land Image (OLI), and WorldView-2 (WV2) at native spatial resolutions of 250 m, 30 m and 2 m respectively and coarser spatial resolution (resampled up to 5 km) to quantify the impact of spatial resolution on the derived TSS product in different turbidity conditions. The results from the study show that in the waters of high turbidity and high spatial variability, the high spatial resolution WV2 sensor reported TSS concentration as high as 160 mg L-1 while the low spatial resolution MODIS-Aqua reported a maximum TSS concentration of 23.6 mg L-1. Degrading the spatial resolution of each satellite sensor for highly spatially variable turbid waters led to variability in the TSS concentrations of 114.46%, 304.68% and 38.2% for WV2, Landsat-8 OLI and MODIS-Aqua respectively. The implications of this work are particularly relevant in the situation of compliance monitoring where operations may be required to restrict TSS concentrations to a pre-defined limit.

Impact of the spatial resolution of satellite remote sensing sensors in the quantification of total suspended sediment concentration: A case study in turbid waters of Northern Western Australia

PubMed Central

Fearns, Peter

2017-01-01

The impact of anthropogenic activities on coastal waters is a cause of concern because such activities add to the total suspended sediment (TSS) budget of the coastal waters, which have negative impacts on the coastal ecosystem. Satellite remote sensing provides a powerful tool in monitoring TSS concentration at high spatiotemporal resolution, but coastal managers should be mindful that the satellite-derived TSS concentrations are dependent on the satellite sensor’s radiometric properties, atmospheric correction approaches, the spatial resolution and the limitations of specific TSS algorithms. In this study, we investigated the impact of different spatial resolutions of satellite sensor on the quantification of TSS concentration in coastal waters of northern Western Australia. We quantified the TSS product derived from MODerate resolution Imaging Spectroradiometer (MODIS)-Aqua, Landsat-8 Operational Land Image (OLI), and WorldView-2 (WV2) at native spatial resolutions of 250 m, 30 m and 2 m respectively and coarser spatial resolution (resampled up to 5 km) to quantify the impact of spatial resolution on the derived TSS product in different turbidity conditions. The results from the study show that in the waters of high turbidity and high spatial variability, the high spatial resolution WV2 sensor reported TSS concentration as high as 160 mg L-1 while the low spatial resolution MODIS-Aqua reported a maximum TSS concentration of 23.6 mg L-1. Degrading the spatial resolution of each satellite sensor for highly spatially variable turbid waters led to variability in the TSS concentrations of 114.46%, 304.68% and 38.2% for WV2, Landsat-8 OLI and MODIS-Aqua respectively. The implications of this work are particularly relevant in the situation of compliance monitoring where operations may be required to restrict TSS concentrations to a pre-defined limit. PMID:28380059

Multi-scale approaches for high-speed imaging and analysis of large neural populations

PubMed Central

Ahrens, Misha B.; Yuste, Rafael; Peterka, Darcy S.; Paninski, Liam

2017-01-01

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. PMID:28771570

Spatial Downscaling of Alien Species Presences using Machine Learning

NASA Astrophysics Data System (ADS)

Daliakopoulos, Ioannis N.; Katsanevakis, Stelios; Moustakas, Aristides

2017-07-01

Large scale, high-resolution data on alien species distributions are essential for spatially explicit assessments of their environmental and socio-economic impacts, and management interventions for mitigation. However, these data are often unavailable. This paper presents a method that relies on Random Forest (RF) models to distribute alien species presence counts at a finer resolution grid, thus achieving spatial downscaling. A sufficiently large number of RF models are trained using random subsets of the dataset as predictors, in a bootstrapping approach to account for the uncertainty introduced by the subset selection. The method is tested with an approximately 8×8 km2 grid containing floral alien species presence and several indices of climatic, habitat, land use covariates for the Mediterranean island of Crete, Greece. Alien species presence is aggregated at 16×16 km2 and used as a predictor of presence at the original resolution, thus simulating spatial downscaling. Potential explanatory variables included habitat types, land cover richness, endemic species richness, soil type, temperature, precipitation, and freshwater availability. Uncertainty assessment of the spatial downscaling of alien species’ occurrences was also performed and true/false presences and absences were quantified. The approach is promising for downscaling alien species datasets of larger spatial scale but coarse resolution, where the underlying environmental information is available at a finer resolution than the alien species data. Furthermore, the RF architecture allows for tuning towards operationally optimal sensitivity and specificity, thus providing a decision support tool for designing a resource efficient alien species census.

Scaling field data to calibrate and validate moderate spatial resolution remote sensing models

USGS Publications Warehouse

Baccini, A.; Friedl, M.A.; Woodcock, C.E.; Zhu, Z.

2007-01-01

Validation and calibration are essential components of nearly all remote sensing-based studies. In both cases, ground measurements are collected and then related to the remote sensing observations or model results. In many situations, and particularly in studies that use moderate resolution remote sensing, a mismatch exists between the sensor's field of view and the scale at which in situ measurements are collected. The use of in situ measurements for model calibration and validation, therefore, requires a robust and defensible method to spatially aggregate ground measurements to the scale at which the remotely sensed data are acquired. This paper examines this challenge and specifically considers two different approaches for aggregating field measurements to match the spatial resolution of moderate spatial resolution remote sensing data: (a) landscape stratification; and (b) averaging of fine spatial resolution maps. The results show that an empirically estimated stratification based on a regression tree method provides a statistically defensible and operational basis for performing this type of procedure. 

High Speed Computational Ghost Imaging via Spatial Sweeping

NASA Astrophysics Data System (ADS)

Wang, Yuwang; Liu, Yang; Suo, Jinli; Situ, Guohai; Qiao, Chang; Dai, Qionghai

2017-03-01

Computational ghost imaging (CGI) achieves single-pixel imaging by using a Spatial Light Modulator (SLM) to generate structured illuminations for spatially resolved information encoding. The imaging speed of CGI is limited by the modulation frequency of available SLMs, and sets back its practical applications. This paper proposes to bypass this limitation by trading off SLM’s redundant spatial resolution for multiplication of the modulation frequency. Specifically, a pair of galvanic mirrors sweeping across the high resolution SLM multiply the modulation frequency within the spatial resolution gap between SLM and the final reconstruction. A proof-of-principle setup with two middle end galvanic mirrors achieves ghost imaging as fast as 42 Hz at 80 × 80-pixel resolution, 5 times faster than state-of-the-arts, and holds potential for one magnitude further multiplication by hardware upgrading. Our approach brings a significant improvement in the imaging speed of ghost imaging and pushes ghost imaging towards practical applications.

A dynamic aerodynamic resistance approach to calculate high resolution sensible heat fluxes in urban areas

NASA Astrophysics Data System (ADS)

Crawford, Ben; Grimmond, Sue; Kent, Christoph; Gabey, Andrew; Ward, Helen; Sun, Ting; Morrison, William

2017-04-01

Remotely sensed data from satellites have potential to enable high-resolution, automated calculation of urban surface energy balance terms and inform decisions about urban adaptations to environmental change. However, aerodynamic resistance methods to estimate sensible heat flux (QH) in cities using satellite-derived observations of surface temperature are difficult in part due to spatial and temporal variability of the thermal aerodynamic resistance term (rah). In this work, we extend an empirical function to estimate rah using observational data from several cities with a broad range of surface vegetation land cover properties. We then use this function to calculate spatially and temporally variable rah in London based on high-resolution (100 m) land cover datasets and in situ meteorological observations. In order to calculate high-resolution QH based on satellite-observed land surface temperatures, we also develop and employ novel methods to i) apply source area-weighted averaging of surface and meteorological variables across the study spatial domain, ii) calculate spatially variable, high-resolution meteorological variables (wind speed, friction velocity, and Obukhov length), iii) incorporate spatially interpolated urban air temperatures from a distributed sensor network, and iv) apply a modified Monte Carlo approach to assess uncertainties with our results, methods, and input variables. Modeled QH using the aerodynamic resistance method is then compared to in situ observations in central London from a unique network of scintillometers and eddy-covariance measurements.

Estimating Soil Moisture at High Spatial Resolution with Three Radiometric Satellite Products: A Study from a South-Eastern Australian Catchment

NASA Astrophysics Data System (ADS)

Senanayake, I. P.; Yeo, I. Y.; Tangdamrongsub, N.; Willgoose, G. R.; Hancock, G. R.; Wells, T.; Fang, B.; Lakshmi, V.

2017-12-01

Long-term soil moisture datasets at high spatial resolution are important in agricultural, hydrological, and climatic applications. The soil moisture estimates can be achieved using satellite remote sensing observations. However, the satellite soil moisture data are typically available at coarse spatial resolutions ( several tens of km), therefore require further downscaling. Different satellite soil moisture products have to be conjointly employed in developing a consistent time-series of high resolution soil moisture, while the discrepancies amongst different satellite retrievals need to be resolved. This study aims to downscale three different satellite soil moisture products, the Soil Moisture and Ocean Salinity (SMOS, 25 km), the Soil Moisture Active Passive (SMAP, 36 km) and the SMAP-Enhanced (9 km), and to conduct an inter-comparison of the downscaled results. The downscaling approach is developed based on the relationship between the diurnal temperature difference and the daily mean soil moisture content. The approach is applied to two sub-catchments (Krui and Merriwa River) of the Goulburn River catchment in the Upper Hunter region (NSW, Australia) to estimate soil moisture at 1 km resolution for 2015. The three coarse spatial resolution soil moisture products and their downscaled results will be validated with the in-situ observations obtained from the Scaling and Assimilation of Soil Moisture and Streamflow (SASMAS) network. The spatial and temporal patterns of the downscaled results will also be analysed. This study will provide the necessary insights for data selection and bias corrections to maintain the consistency of a long-term high resolution soil moisture dataset. The results will assist in developing a time-series of high resolution soil moisture data over the south-eastern Australia.

Coherent optical adaptive technique improves the spatial resolution of STED microscopy in thick samples

PubMed Central

Yan, Wei; Yang, Yanlong; Tan, Yu; Chen, Xun; Li, Yang; Qu, Junle; Ye, Tong

2018-01-01

Stimulated emission depletion microscopy (STED) is one of far-field optical microscopy techniques that can provide sub-diffraction spatial resolution. The spatial resolution of the STED microscopy is determined by the specially engineered beam profile of the depletion beam and its power. However, the beam profile of the depletion beam may be distorted due to aberrations of optical systems and inhomogeneity of specimens’ optical properties, resulting in a compromised spatial resolution. The situation gets deteriorated when thick samples are imaged. In the worst case, the sever distortion of the depletion beam profile may cause complete loss of the super resolution effect no matter how much depletion power is applied to specimens. Previously several adaptive optics approaches have been explored to compensate aberrations of systems and specimens. However, it is hard to correct the complicated high-order optical aberrations of specimens. In this report, we demonstrate that the complicated distorted wavefront from a thick phantom sample can be measured by using the coherent optical adaptive technique (COAT). The full correction can effectively maintain and improve the spatial resolution in imaging thick samples. PMID:29400356

A multi-temporal analysis approach for land cover mapping in support of nuclear incident response

NASA Astrophysics Data System (ADS)

Sah, Shagan; van Aardt, Jan A. N.; McKeown, Donald M.; Messinger, David W.

2012-06-01

Remote sensing can be used to rapidly generate land use maps for assisting emergency response personnel with resource deployment decisions and impact assessments. In this study we focus on constructing accurate land cover maps to map the impacted area in the case of a nuclear material release. The proposed methodology involves integration of results from two different approaches to increase classification accuracy. The data used included RapidEye scenes over Nine Mile Point Nuclear Power Station (Oswego, NY). The first step was building a coarse-scale land cover map from freely available, high temporal resolution, MODIS data using a time-series approach. In the case of a nuclear accident, high spatial resolution commercial satellites such as RapidEye or IKONOS can acquire images of the affected area. Land use maps from the two image sources were integrated using a probability-based approach. Classification results were obtained for four land classes - forest, urban, water and vegetation - using Euclidean and Mahalanobis distances as metrics. Despite the coarse resolution of MODIS pixels, acceptable accuracies were obtained using time series features. The overall accuracies using the fusion based approach were in the neighborhood of 80%, when compared with GIS data sets from New York State. The classifications were augmented using this fused approach, with few supplementary advantages such as correction for cloud cover and independence from time of year. We concluded that this method would generate highly accurate land maps, using coarse spatial resolution time series satellite imagery and a single date, high spatial resolution, multi-spectral image.

Multimodal hard x-ray imaging with resolution approaching 10 nm for studies in material science

NASA Astrophysics Data System (ADS)

Yan, Hanfei; Bouet, Nathalie; Zhou, Juan; Huang, Xiaojing; Nazaretski, Evgeny; Xu, Weihe; Cocco, Alex P.; Chiu, Wilson K. S.; Brinkman, Kyle S.; Chu, Yong S.

2018-03-01

We report multimodal scanning hard x-ray imaging with spatial resolution approaching 10 nm and its application to contemporary studies in the field of material science. The high spatial resolution is achieved by focusing hard x-rays with two crossed multilayer Laue lenses and raster-scanning a sample with respect to the nanofocusing optics. Various techniques are used to characterize and verify the achieved focus size and imaging resolution. The multimodal imaging is realized by utilizing simultaneously absorption-, phase-, and fluorescence-contrast mechanisms. The combination of high spatial resolution and multimodal imaging enables a comprehensive study of a sample on a very fine length scale. In this work, the unique multimodal imaging capability was used to investigate a mixed ionic-electronic conducting ceramic-based membrane material employed in solid oxide fuel cells and membrane separations (compound of Ce0.8Gd0.2O2‑x and CoFe2O4) which revealed the existence of an emergent material phase and quantified the chemical complexity at the nanoscale.

A hybrid color mapping approach to fusing MODIS and Landsat images for forward prediction

USDA-ARS?s Scientific Manuscript database

We present a new, simple, and efficient approach to fusing MODIS and Landsat images. It is well known that MODIS images have high temporal resolution and low spatial resolution whereas Landsat images are just the opposite. Similar to earlier approaches, our goal is to fuse MODIS and Landsat images t...

Enhanced Satellite Remote Sensing of Coastal Waters Using Spatially Improved Bio-Optical Products from SNPP-VIIRS

DTIC Science & Technology

2015-01-01

a spatial resolution of 250-m. The Gumley et al. computation for MODIS sharpening is given as a ratio of high to low resolution top of the atmosphere...NIR) correction (Stumpf, Arnone, Gould, Martinolich, & Ransibrahamanakul, 2003). Standard flagswere used tomask interference from land, clouds , sun...technique This new approach expands on the methodology described by Gumley et al. (2010), with somemodifications. We will compute a sim- ilar spatial

The x-ray light valve: a low-cost, digital radiographic imaging system-spatial resolution

NASA Astrophysics Data System (ADS)

MacDougall, Robert D.; Koprinarov, Ivaylo; Webster, Christie A.; Rowlands, J. A.

2007-03-01

In recent years, new x-ray radiographic systems based on large area flat panel technology have revolutionized our capability to produce digital x-ray radiographic images. However, these active matrix flat panel imagers (AMFPIs) are extraordinarily expensive compared to the systems they are replacing. Thus there is a need for a low cost digital imaging system for general applications in radiology. Different approaches have been considered to make lower cost, integrated x-ray imaging devices for digital radiography, including: scanned projection x-ray, an integrated approach based on computed radiography technology and optically demagnified x-ray screen/CCD systems. These approaches suffer from either high cost or high mechanical complexity and do not have the image quality of AMFPIs. We have identified a new approach - the X-ray Light Valve (XLV). The XLV has the potential to achieve the immediate readout in an integrated system with image quality comparable to AMFPIs. The XLV concept combines three well-established and hence lowcost technologies: an amorphous selenium (a-Se) layer to convert x-rays to image charge, a liquid crystal (LC) cell as an analog display, and an optical scanner for image digitization. Here we investigate the spatial resolution possible with XLV systems. Both a-Se and LC cells have both been shown separately to have inherently very high spatial resolution. Due to the close electrostatic coupling in the XLV, it can be expected that the spatial resolution of this system will also be very high. A prototype XLV was made and a typical office scanner was used for image digitization. The Modulation Transfer Function was measured and the limiting factor was seen to be the optical scanner. However, even with this limitation the XLV system is able to meet or exceed the resolution requirements for chest radiography.

A multi-temporal fusion-based approach for land cover mapping in support of nuclear incident response

NASA Astrophysics Data System (ADS)

Sah, Shagan

An increasingly important application of remote sensing is to provide decision support during emergency response and disaster management efforts. Land cover maps constitute one such useful application product during disaster events; if generated rapidly after any disaster, such map products can contribute to the efficacy of the response effort. In light of recent nuclear incidents, e.g., after the earthquake/tsunami in Japan (2011), our research focuses on constructing rapid and accurate land cover maps of the impacted area in case of an accidental nuclear release. The methodology involves integration of results from two different approaches, namely coarse spatial resolution multi-temporal and fine spatial resolution imagery, to increase classification accuracy. Although advanced methods have been developed for classification using high spatial or temporal resolution imagery, only a limited amount of work has been done on fusion of these two remote sensing approaches. The presented methodology thus involves integration of classification results from two different remote sensing modalities in order to improve classification accuracy. The data used included RapidEye and MODIS scenes over the Nine Mile Point Nuclear Power Station in Oswego (New York, USA). The first step in the process was the construction of land cover maps from freely available, high temporal resolution, low spatial resolution MODIS imagery using a time-series approach. We used the variability in the temporal signatures among different land cover classes for classification. The time series-specific features were defined by various physical properties of a pixel, such as variation in vegetation cover and water content over time. The pixels were classified into four land cover classes - forest, urban, water, and vegetation - using Euclidean and Mahalanobis distance metrics. On the other hand, a high spatial resolution commercial satellite, such as RapidEye, can be tasked to capture images over the affected area in the case of a nuclear event. This imagery served as a second source of data to augment results from the time series approach. The classifications from the two approaches were integrated using an a posteriori probability-based fusion approach. This was done by establishing a relationship between the classes, obtained after classification of the two data sources. Despite the coarse spatial resolution of MODIS pixels, acceptable accuracies were obtained using time series features. The overall accuracies using the fusion-based approach were in the neighborhood of 80%, when compared with GIS data sets from New York State. This fusion thus contributed to classification accuracy refinement, with a few additional advantages, such as correction for cloud cover and providing for an approach that is robust against point-in-time seasonal anomalies, due to the inclusion of multi-temporal data. We concluded that this approach is capable of generating land cover maps of acceptable accuracy and rapid turnaround, which in turn can yield reliable estimates of crop acreage of a region. The final algorithm is part of an automated software tool, which can be used by emergency response personnel to generate a nuclear ingestion pathway information product within a few hours of data collection.

Resolution convergence in cosmological hydrodynamical simulations using adaptive mesh refinement

NASA Astrophysics Data System (ADS)

Snaith, Owain N.; Park, Changbom; Kim, Juhan; Rosdahl, Joakim

2018-06-01

We have explored the evolution of gas distributions from cosmological simulations carried out using the RAMSES adaptive mesh refinement (AMR) code, to explore the effects of resolution on cosmological hydrodynamical simulations. It is vital to understand the effect of both the resolution of initial conditions (ICs) and the final resolution of the simulation. Lower initial resolution simulations tend to produce smaller numbers of low-mass structures. This will strongly affect the assembly history of objects, and has the same effect of simulating different cosmologies. The resolution of ICs is an important factor in simulations, even with a fixed maximum spatial resolution. The power spectrum of gas in simulations using AMR diverges strongly from the fixed grid approach - with more power on small scales in the AMR simulations - even at fixed physical resolution and also produces offsets in the star formation at specific epochs. This is because before certain times the upper grid levels are held back to maintain approximately fixed physical resolution, and to mimic the natural evolution of dark matter only simulations. Although the impact of hold-back falls with increasing spatial and IC resolutions, the offsets in the star formation remain down to a spatial resolution of 1 kpc. These offsets are of the order of 10-20 per cent, which is below the uncertainty in the implemented physics but are expected to affect the detailed properties of galaxies. We have implemented a new grid-hold-back approach to minimize the impact of hold-back on the star formation rate.

Enhancing resolution and contrast in second-harmonic generation microscopy using an advanced maximum likelihood estimation restoration method

NASA Astrophysics Data System (ADS)

Sivaguru, Mayandi; Kabir, Mohammad M.; Gartia, Manas Ranjan; Biggs, David S. C.; Sivaguru, Barghav S.; Sivaguru, Vignesh A.; Berent, Zachary T.; Wagoner Johnson, Amy J.; Fried, Glenn A.; Liu, Gang Logan; Sadayappan, Sakthivel; Toussaint, Kimani C.

2017-02-01

Second-harmonic generation (SHG) microscopy is a label-free imaging technique to study collagenous materials in extracellular matrix environment with high resolution and contrast. However, like many other microscopy techniques, the actual spatial resolution achievable by SHG microscopy is reduced by out-of-focus blur and optical aberrations that degrade particularly the amplitude of the detectable higher spatial frequencies. Being a two-photon scattering process, it is challenging to define a point spread function (PSF) for the SHG imaging modality. As a result, in comparison with other two-photon imaging systems like two-photon fluorescence, it is difficult to apply any PSF-engineering techniques to enhance the experimental spatial resolution closer to the diffraction limit. Here, we present a method to improve the spatial resolution in SHG microscopy using an advanced maximum likelihood estimation (AdvMLE) algorithm to recover the otherwise degraded higher spatial frequencies in an SHG image. Through adaptation and iteration, the AdvMLE algorithm calculates an improved PSF for an SHG image and enhances the spatial resolution by decreasing the full-width-at-halfmaximum (FWHM) by 20%. Similar results are consistently observed for biological tissues with varying SHG sources, such as gold nanoparticles and collagen in porcine feet tendons. By obtaining an experimental transverse spatial resolution of 400 nm, we show that the AdvMLE algorithm brings the practical spatial resolution closer to the theoretical diffraction limit. Our approach is suitable for adaptation in micro-nano CT and MRI imaging, which has the potential to impact diagnosis and treatment of human diseases.

Study on the Spatial Resolution of Single and Multiple Coincidences Compton Camera

NASA Astrophysics Data System (ADS)

Andreyev, Andriy; Sitek, Arkadiusz; Celler, Anna

2012-10-01

In this paper we study the image resolution that can be obtained from the Multiple Coincidences Compton Camera (MCCC). The principle of MCCC is based on a simultaneous acquisition of several gamma-rays emitted in cascade from a single nucleus. Contrary to a standard Compton camera, MCCC can theoretically provide the exact location of a radioactive source (based only on the identification of the intersection point of three cones created by a single decay), without complicated tomographic reconstruction. However, practical implementation of the MCCC approach encounters several problems, such as low detection sensitivities result in very low probability of coincident triple gamma-ray detection, which is necessary for the source localization. It is also important to evaluate how the detection uncertainties (finite energy and spatial resolution) influence identification of the intersection of three cones, thus the resulting image quality. In this study we investigate how the spatial resolution of the reconstructed images using the triple-cone reconstruction (TCR) approach compares to images reconstructed from the same data using standard iterative method based on single-cone. Results show, that FWHM for the point source reconstructed with TCR was 20-30% higher than the one obtained from the standard iterative reconstruction based on expectation maximization (EM) algorithm and conventional single-cone Compton imaging. Finite energy and spatial resolutions of the MCCC detectors lead to errors in conical surfaces definitions (“thick” conical surfaces) which only amplify in image reconstruction when intersection of three cones is being sought. Our investigations show that, in spite of being conceptually appealing, the identification of triple cone intersection constitutes yet another restriction of the multiple coincidence approach which limits the image resolution that can be obtained with MCCC and TCR algorithm.

Lensfree on-chip microscopy over a wide field-of-view using pixel super-resolution

PubMed Central

Bishara, Waheb; Su, Ting-Wei; Coskun, Ahmet F.; Ozcan, Aydogan

2010-01-01

We demonstrate lensfree holographic microscopy on a chip to achieve ~0.6 µm spatial resolution corresponding to a numerical aperture of ~0.5 over a large field-of-view of ~24 mm2. By using partially coherent illumination from a large aperture (~50 µm), we acquire lower resolution lensfree in-line holograms of the objects with unit fringe magnification. For each lensfree hologram, the pixel size at the sensor chip limits the spatial resolution of the reconstructed image. To circumvent this limitation, we implement a sub-pixel shifting based super-resolution algorithm to effectively recover much higher resolution digital holograms of the objects, permitting sub-micron spatial resolution to be achieved across the entire sensor chip active area, which is also equivalent to the imaging field-of-view (24 mm2) due to unit magnification. We demonstrate the success of this pixel super-resolution approach by imaging patterned transparent substrates, blood smear samples, as well as Caenoharbditis Elegans. PMID:20588977

Partial Least Square Discriminant Analysis Based on Normalized Two-Stage Vegetation Indices for Mapping Damage from Rice Diseases Using PlanetScope Datasets.

PubMed

Shi, Yue; Huang, Wenjiang; Ye, Huichun; Ruan, Chao; Xing, Naichen; Geng, Yun; Dong, Yingying; Peng, Dailiang

2018-06-11

In recent decades, rice disease co-epidemics have caused tremendous damage to crop production in both China and Southeast Asia. A variety of remote sensing based approaches have been developed and applied to map diseases distribution using coarse- to moderate-resolution imagery. However, the detection and discrimination of various disease species infecting rice were seldom assessed using high spatial resolution data. The aims of this study were (1) to develop a set of normalized two-stage vegetation indices (VIs) for characterizing the progressive development of different diseases with rice; (2) to explore the performance of combined normalized two-stage VIs in partial least square discriminant analysis (PLS-DA); and (3) to map and evaluate the damage caused by rice diseases at fine spatial scales, for the first time using bi-temporal, high spatial resolution imagery from PlanetScope datasets at a 3 m spatial resolution. Our findings suggest that the primary biophysical parameters caused by different disease (e.g., changes in leaf area, pigment contents, or canopy morphology) can be captured using combined normalized two-stage VIs. PLS-DA was able to classify rice diseases at a sub-field scale, with an overall accuracy of 75.62% and a Kappa value of 0.47. The approach was successfully applied during a typical co-epidemic outbreak of rice dwarf (Rice dwarf virus, RDV), rice blast ( Magnaporthe oryzae ), and glume blight ( Phyllosticta glumarum ) in Guangxi Province, China. Furthermore, our approach highlighted the feasibility of the method in capturing heterogeneous disease patterns at fine spatial scales over the large spatial extents.

Using High Spatial Resolution Satellite Imagery to Map Forest Burn Severity Across Spatial Scales in a Pine Barrens Ecosystem

NASA Technical Reports Server (NTRS)

Meng, Ran; Wu, Jin; Schwager, Kathy L.; Zhao, Feng; Dennison, Philip E.; Cook, Bruce D.; Brewster, Kristen; Green, Timothy M.; Serbin, Shawn P.

2017-01-01

As a primary disturbance agent, fire significantly influences local processes and services of forest ecosystems. Although a variety of remote sensing based approaches have been developed and applied to Landsat mission imagery to infer burn severity at 30 m spatial resolution, forest burn severity have still been seldom assessed at fine spatial scales (less than or equal to 5 m) from very-high-resolution (VHR) data. We assessed a 432 ha forest fire that occurred in April 2012 on Long Island, New York, within the Pine Barrens region, a unique but imperiled fire-dependent ecosystem in the northeastern United States. The mapping of forest burn severity was explored here at fine spatial scales, for the first time using remotely sensed spectral indices and a set of Multiple Endmember Spectral Mixture Analysis (MESMA) fraction images from bi-temporal - pre- and post-fire event - WorldView-2 (WV-2) imagery at 2 m spatial resolution. We first evaluated our approach using 1 m by 1 m validation points at the sub-crown scale per severity class (i.e. unburned, low, moderate, and high severity) from the post-fire 0.10 m color aerial ortho-photos; then, we validated the burn severity mapping of geo-referenced dominant tree crowns (crown scale) and 15 m by 15 m fixed-area plots (inter-crown scale) with the post-fire 0.10 m aerial ortho-photos and measured crown information of twenty forest inventory plots. Our approach can accurately assess forest burn severity at the sub-crown (overall accuracy is 84% with a Kappa value of 0.77), crown (overall accuracy is 82% with a Kappa value of 0.76), and inter-crown scales (89% of the variation in estimated burn severity ratings (i.e. Geo-Composite Burn Index (CBI)). This work highlights that forest burn severity mapping from VHR data can capture heterogeneous fire patterns at fine spatial scales over the large spatial extents. This is important since most ecological processes associated with fire effects vary at the less than 30 m scale and VHR approaches could significantly advance our ability to characterize fire effects on forest ecosystems.

Using high spatial resolution satellite imagery to map forest burn severity across spatial scales in a Pine Barrens ecosystem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meng, Ran; Wu, Jin; Schwager, Kathy L.

As a primary disturbance agent, fire significantly influences local processes and services of forest ecosystems. Although a variety of remote sensing based approaches have been developed and applied to Landsat mission imagery to infer burn severity at 30 m spatial resolution, forest burn severity have still been seldom assessed at fine spatial scales (≤ 5 m) from very-high-resolution (VHR) data. Here we assessed a 432 ha forest fire that occurred in April 2012 on Long Island, New York, within the Pine Barrens region, a unique but imperiled fire-dependent ecosystem in the northeastern United States. The mapping of forest burn severitymore » was explored here at fine spatial scales, for the first time using remotely sensed spectral indices and a set of Multiple Endmember Spectral Mixture Analysis (MESMA) fraction images from bi-temporal — pre- and post-fire event — WorldView-2 (WV-2) imagery at 2 m spatial resolution. We first evaluated our approach using 1 m by 1 m validation points at the sub-crown scale per severity class (i.e. unburned, low, moderate, and high severity) from the post-fire 0.10 m color aerial ortho-photos; then, we validated the burn severity mapping of geo-referenced dominant tree crowns (crown scale) and 15 m by 15 m fixed-area plots (inter-crown scale) with the post-fire 0.10 m aerial ortho-photos and measured crown information of twenty forest inventory plots. Our approach can accurately assess forest burn severity at the sub-crown (overall accuracy is 84% with a Kappa value of 0.77), crown (overall accuracy is 82% with a Kappa value of 0.76), and inter-crown scales (89% of the variation in estimated burn severity ratings (i.e. Geo-Composite Burn Index (CBI)). Lastly, this work highlights that forest burn severity mapping from VHR data can capture heterogeneous fire patterns at fine spatial scales over the large spatial extents. This is important since most ecological processes associated with fire effects vary at the < 30 m scale and VHR approaches could significantly advance our ability to characterize fire effects on forest ecosystems.« less

Using high spatial resolution satellite imagery to map forest burn severity across spatial scales in a Pine Barrens ecosystem

DOE PAGES

Meng, Ran; Wu, Jin; Schwager, Kathy L.; ...

2017-01-21

As a primary disturbance agent, fire significantly influences local processes and services of forest ecosystems. Although a variety of remote sensing based approaches have been developed and applied to Landsat mission imagery to infer burn severity at 30 m spatial resolution, forest burn severity have still been seldom assessed at fine spatial scales (≤ 5 m) from very-high-resolution (VHR) data. Here we assessed a 432 ha forest fire that occurred in April 2012 on Long Island, New York, within the Pine Barrens region, a unique but imperiled fire-dependent ecosystem in the northeastern United States. The mapping of forest burn severitymore » was explored here at fine spatial scales, for the first time using remotely sensed spectral indices and a set of Multiple Endmember Spectral Mixture Analysis (MESMA) fraction images from bi-temporal — pre- and post-fire event — WorldView-2 (WV-2) imagery at 2 m spatial resolution. We first evaluated our approach using 1 m by 1 m validation points at the sub-crown scale per severity class (i.e. unburned, low, moderate, and high severity) from the post-fire 0.10 m color aerial ortho-photos; then, we validated the burn severity mapping of geo-referenced dominant tree crowns (crown scale) and 15 m by 15 m fixed-area plots (inter-crown scale) with the post-fire 0.10 m aerial ortho-photos and measured crown information of twenty forest inventory plots. Our approach can accurately assess forest burn severity at the sub-crown (overall accuracy is 84% with a Kappa value of 0.77), crown (overall accuracy is 82% with a Kappa value of 0.76), and inter-crown scales (89% of the variation in estimated burn severity ratings (i.e. Geo-Composite Burn Index (CBI)). Lastly, this work highlights that forest burn severity mapping from VHR data can capture heterogeneous fire patterns at fine spatial scales over the large spatial extents. This is important since most ecological processes associated with fire effects vary at the < 30 m scale and VHR approaches could significantly advance our ability to characterize fire effects on forest ecosystems.« less

Evaluating Climate Causation of Conflict in Darfur Using Multi-temporal, Multi-resolution Satellite Image Datasets With Novel Analyses

NASA Astrophysics Data System (ADS)

Brown, I.; Wennbom, M.

2013-12-01

Climate change, population growth and changes in traditional lifestyles have led to instabilities in traditional demarcations between neighboring ethic and religious groups in the Sahel region. This has resulted in a number of conflicts as groups resort to arms to settle disputes. Such disputes often centre on or are justified by competition for resources. The conflict in Darfur has been controversially explained by resource scarcity resulting from climate change. Here we analyse established methods of using satellite imagery to assess vegetation health in Darfur. Multi-decadal time series of observations are available using low spatial resolution visible-near infrared imagery. Typically normalized difference vegetation index (NDVI) analyses are produced to describe changes in vegetation ';greenness' or ';health'. Such approaches have been widely used to evaluate the long term development of vegetation in relation to climate variations across a wide range of environments from the Arctic to the Sahel. These datasets typically measure peak NDVI observed over a given interval and may introduce bias. It is furthermore unclear how the spatial organization of sparse vegetation may affect low resolution NDVI products. We develop and assess alternative measures of vegetation including descriptors of the growing season, wetness and resource availability. Expanding the range of parameters used in the analysis reduces our dependence on peak NDVI. Furthermore, these descriptors provide a better characterization of the growing season than the single NDVI measure. Using multi-sensor data we combine high temporal/moderate spatial resolution data with low temporal/high spatial resolution data to improve the spatial representativity of the observations and to provide improved spatial analysis of vegetation patterns. The approach places the high resolution observations in the NDVI context space using a longer time series of lower resolution imagery. The vegetation descriptors derived are evaluated using independent high spatial resolution datasets that reveal the pattern and health of vegetation at metre scales. We also use climate variables to support the interpretation of these data. We conclude that the spatio-temporal patterns in Darfur vegetation and climate datasets suggest that labelling the conflict a climate-change conflict is inaccurate and premature.

Combining multiple approaches and optimized data resolution for an improved understanding of stream temperature dynamics of a forested headwater basin in the Southern Appalachians

NASA Astrophysics Data System (ADS)

Belica, L.; Mitasova, H.; Caldwell, P.; McCarter, J. B.; Nelson, S. A. C.

2017-12-01

Thermal regimes of forested headwater streams continue to be an area of active research as climatic, hydrologic, and land cover changes can influence water temperature, a key aspect of aquatic ecosystems. Widespread monitoring of stream temperatures have provided an important data source, yielding insights on the temporal and spatial patterns and the underlying processes that influence stream temperature. However, small forested streams remain challenging to model due to the high spatial and temporal variability of stream temperatures and the climatic and hydrologic conditions that drive them. Technological advances and increased computational power continue to provide new tools and measurement methods and have allowed spatially explicit analyses of dynamic natural systems at greater temporal resolutions than previously possible. With the goal of understanding how current stream temperature patterns and processes may respond to changing landcover and hydroclimatoligical conditions, we combined high-resolution, spatially explicit geospatial modeling with deterministic heat flux modeling approaches using data sources that ranged from traditional hydrological and climatological measurements to emerging remote sensing techniques. Initial analyses of stream temperature monitoring data revealed that high temporal resolution (5 minutes) and measurement resolutions (<0.1°C) were needed to adequately describe diel stream temperature patterns and capture the differences between paired 1st order and 4th order forest streams draining north and south facing slopes. This finding along with geospatial models of subcanopy solar radiation and channel morphology were used to develop hypotheses and guide field data collection for further heat flux modeling. By integrating multiple approaches and optimizing data resolution for the processes being investigated, small, but ecologically significant differences in stream thermal regimes were revealed. In this case, multi-approach research contributed to the identification of the dominant mechanisms driving stream temperature in the study area and advanced our understanding of the current thermal fluxes and how they may change as environmental conditions change in the future.

Integrative Structure Determination of Protein Assemblies by Satisfaction of Spatial Restraints

NASA Astrophysics Data System (ADS)

Alber, Frank; Chait, Brian T.; Rout, Michael P.; Sali, Andrej

To understand the cell, we need to determine the structures of macromolecular assemblies, many of which consist of tens to hundreds of components. A great variety of experimental data can be used to characterize the assemblies at several levels of resolution, from atomic structures to component configurations. To maximize completeness, resolution, accuracy, precision and efficiency of the structure determination, a computational approach is needed that can use spatial information from a variety of experimental methods. We propose such an approach, defined by its three main components: a hierarchical representation of the assembly, a scoring function consisting of spatial restraints derived from experimental data, and an optimization method that generates structures consistent with the data. We illustrate the approach by determining the configuration of the 456 proteins in the nuclear pore complex from Baker's yeast.

Signatures of Penumbral Magnetic Fields at Very High Spatial Resolution

NASA Astrophysics Data System (ADS)

Langhans, K.

2006-12-01

Full Stokes spectro-polarimetry, together with refined techniques to interpret the measurements and continual modeling efforts, have improved our understanding of sunspot penumbrae in the last years. In spite of this progress, an improvement in the spatial resolution of the observations is clearly needed to establish in a more direct way the fine structure of the penumbra. The discovery of dark penumbral cores by tet{l3 Sc02} suggests that we are starting to resolve the fundamental scales of the penumbra. Spectro-polarimetric measurements that are sensitive to the magnetic field in both the photosphere and higher layers, and obtained at a spatial resolution approaching 0.1 arcsec, may therefore allow us to draw firm conclusions about the fine scale organization of penumbral magnetic fields. In this paper I will discuss recent polarization measurements at very high spatial resolution, trying to reconcile the different scenarios put forward to explain the structure of the penumbra.

ML-Space: Hybrid Spatial Gillespie and Particle Simulation of Multi-Level Rule-Based Models in Cell Biology.

PubMed

Bittig, Arne T; Uhrmacher, Adelinde M

2017-01-01

Spatio-temporal dynamics of cellular processes can be simulated at different levels of detail, from (deterministic) partial differential equations via the spatial Stochastic Simulation algorithm to tracking Brownian trajectories of individual particles. We present a spatial simulation approach for multi-level rule-based models, which includes dynamically hierarchically nested cellular compartments and entities. Our approach ML-Space combines discrete compartmental dynamics, stochastic spatial approaches in discrete space, and particles moving in continuous space. The rule-based specification language of ML-Space supports concise and compact descriptions of models and to adapt the spatial resolution of models easily.

Spatially-controlled illumination with rescan confocal microscopy enhances image quality, resolution and reduces photodamage

NASA Astrophysics Data System (ADS)

Krishnaswami, Venkataraman; De Luca, Giulia M. R.; Breedijk, Ronald M. P.; Van Noorden, Cornelis J. F.; Manders, Erik M. M.; Hoebe, Ron A.

2017-02-01

Fluorescence microscopy is an important tool in biomedical imaging. An inherent trade-off lies between image quality and photodamage. Recently, we have introduced rescan confocal microscopy (RCM) that improves the lateral resolution of a confocal microscope down to 170 nm. Previously, we have demonstrated that with controlled-light exposure microscopy, spatial control of illumination reduces photodamage without compromising image quality. Here, we show that the combination of these two techniques leads to high resolution imaging with reduced photodamage without compromising image quality. Implementation of spatially-controlled illumination was carried out in RCM using a line scanning-based approach. Illumination is spatially-controlled for every line during imaging with the help of a prediction algorithm that estimates the spatial profile of the fluorescent specimen. The estimation is based on the information available from previously acquired line images. As a proof-of-principle, we show images of N1E-115 neuroblastoma cells, obtained by this new setup with reduced illumination dose, improved resolution and without compromising image quality.

The spatial resolving power of earth resources satellites: A review

NASA Technical Reports Server (NTRS)

Townshend, J. R. G.

1980-01-01

The significance of spatial resolving power on the utility of current and future Earth resources satellites is critically discussed and the relative merits of different approaches in defining and estimating spatial resolution are outlined. It is shown that choice of a particular measure of spatial resolution depends strongly on the particular needs of the user. Several experiments have simulated the capabilities of future satellite systems by degradation of aircraft images. Surprisingly, many of these indicated that improvements in resolution may lead to a reduction in the classification accuracy of land cover types using computer assisted methods. However, where the frequency of boundary pixels is high, the converse relationship is found. Use of imagery dependent upon visual interpretation is likely to benefit more consistently from higher resolutions. Extraction of information from images will depend upon several other factors apart from spatial resolving power: these include characteristics of the terrain being sensed, the image processing methods that are applied as well as certain sensor characteristics.

Electric crosstalk impairs spatial resolution of multi-electrode arrays in retinal implants

NASA Astrophysics Data System (ADS)

Wilke, R. G. H.; Khalili Moghadam, G.; Lovell, N. H.; Suaning, G. J.; Dokos, S.

2011-08-01

Active multi-electrode arrays are used in vision prostheses, including optic nerve cuffs and cortical and retinal implants for stimulation of neural tissue. For retinal implants, arrays with up to 1500 electrodes are used in clinical trials. The ability to convey information with high spatial resolution is critical for these applications. To assess the extent to which spatial resolution is impaired by electric crosstalk, finite-element simulation of electric field distribution in a simplified passive tissue model of the retina is performed. The effects of electrode size, electrode spacing, distance to target cells, and electrode return configuration (monopolar, tripolar, hexagonal) on spatial resolution is investigated in the form of a mathematical model of electric field distribution. Results show that spatial resolution is impaired with increased distance from the electrode array to the target cells. This effect can be partly compensated by non-monopolar electrode configurations and larger electrode diameters, albeit at the expense of lower pixel densities due to larger covering areas by each stimulation electrode. In applications where multi-electrode arrays can be brought into close proximity to target cells, as presumably with epiretinal implants, smaller electrodes in monopolar configuration can provide the highest spatial resolution. However, if the implantation site is further from the target cells, as is the case in suprachoroidal approaches, hexagonally guarded electrode return configurations can convey higher spatial resolution. This paper was originally submitted for the special issue containing contributions from the Sixth Biennial Research Congress of The Eye and the Chip.

Assessment of Spatiotemporal Fusion Algorithms for Planet and Worldview Images

PubMed Central

Zhu, Xiaolin; Gao, Feng; Chou, Bryan; Li, Jiang; Shen, Yuzhong; Koperski, Krzysztof; Marchisio, Giovanni

2018-01-01

Although Worldview-2 (WV) images (non-pansharpened) have 2-m resolution, the re-visit times for the same areas may be seven days or more. In contrast, Planet images are collected using small satellites that can cover the whole Earth almost daily. However, the resolution of Planet images is 3.125 m. It would be ideal to fuse these two satellites images to generate high spatial resolution (2 m) and high temporal resolution (1 or 2 days) images for applications such as damage assessment, border monitoring, etc. that require quick decisions. In this paper, we evaluate three approaches to fusing Worldview (WV) and Planet images. These approaches are known as Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal Data Fusion (FSDAF), and Hybrid Color Mapping (HCM), which have been applied to the fusion of MODIS and Landsat images in recent years. Experimental results using actual Planet and Worldview images demonstrated that the three aforementioned approaches have comparable performance and can all generate high quality prediction images. PMID:29614745

Assessment of Spatiotemporal Fusion Algorithms for Planet and Worldview Images.

PubMed

Kwan, Chiman; Zhu, Xiaolin; Gao, Feng; Chou, Bryan; Perez, Daniel; Li, Jiang; Shen, Yuzhong; Koperski, Krzysztof; Marchisio, Giovanni

2018-03-31

Although Worldview-2 (WV) images (non-pansharpened) have 2-m resolution, the re-visit times for the same areas may be seven days or more. In contrast, Planet images are collected using small satellites that can cover the whole Earth almost daily. However, the resolution of Planet images is 3.125 m. It would be ideal to fuse these two satellites images to generate high spatial resolution (2 m) and high temporal resolution (1 or 2 days) images for applications such as damage assessment, border monitoring, etc. that require quick decisions. In this paper, we evaluate three approaches to fusing Worldview (WV) and Planet images. These approaches are known as Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal Data Fusion (FSDAF), and Hybrid Color Mapping (HCM), which have been applied to the fusion of MODIS and Landsat images in recent years. Experimental results using actual Planet and Worldview images demonstrated that the three aforementioned approaches have comparable performance and can all generate high quality prediction images.

Horizontal Residual Mean Circulation: Evaluation of Spatial Correlations in Coarse Resolution Ocean Models

NASA Astrophysics Data System (ADS)

Li, Y.; McDougall, T. J.

2016-02-01

Coarse resolution ocean models lack knowledge of spatial correlations between variables on scales smaller than the grid scale. Some researchers have shown that these spatial correlations play a role in the poleward heat flux. In order to evaluate the poleward transport induced by the spatial correlations at a fixed horizontal position, an equation is obtained to calculate the approximate transport from velocity gradients. The equation involves two terms that can be added to the quasi-Stokes streamfunction (based on temporal correlations) to incorporate the contribution of spatial correlations. Moreover, these new terms do not need to be parameterized and is ready to be evaluated by using model data directly. In this study, data from a high resolution ocean model have been used to estimate the accuracy of this HRM approach for improving the horizontal property fluxes in coarse-resolution ocean models. A coarse grid is formed by sub-sampling and box-car averaging the fine grid scale. The transport calculated on the coarse grid is then compared to the transport on original high resolution grid scale accumulated over a corresponding number of grid boxes. The preliminary results have shown that the estimate on coarse resolution grids roughly match the corresponding transports on high resolution grids.

Spectral-spatial hyperspectral image classification using super-pixel-based spatial pyramid representation

NASA Astrophysics Data System (ADS)

Fan, Jiayuan; Tan, Hui Li; Toomik, Maria; Lu, Shijian

2016-10-01

Spatial pyramid matching has demonstrated its power for image recognition task by pooling features from spatially increasingly fine sub-regions. Motivated by the concept of feature pooling at multiple pyramid levels, we propose a novel spectral-spatial hyperspectral image classification approach using superpixel-based spatial pyramid representation. This technique first generates multiple superpixel maps by decreasing the superpixel number gradually along with the increased spatial regions for labelled samples. By using every superpixel map, sparse representation of pixels within every spatial region is then computed through local max pooling. Finally, features learned from training samples are aggregated and trained by a support vector machine (SVM) classifier. The proposed spectral-spatial hyperspectral image classification technique has been evaluated on two public hyperspectral datasets, including the Indian Pines image containing 16 different agricultural scene categories with a 20m resolution acquired by AVIRIS and the University of Pavia image containing 9 land-use categories with a 1.3m spatial resolution acquired by the ROSIS-03 sensor. Experimental results show significantly improved performance compared with the state-of-the-art works. The major contributions of this proposed technique include (1) a new spectral-spatial classification approach to generate feature representation for hyperspectral image, (2) a complementary yet effective feature pooling approach, i.e. the superpixel-based spatial pyramid representation that is used for the spatial correlation study, (3) evaluation on two public hyperspectral image datasets with superior image classification performance.

High-resolution whole-brain DCE-MRI using constrained reconstruction: Prospective clinical evaluation in brain tumor patients.

PubMed

Guo, Yi; Lebel, R Marc; Zhu, Yinghua; Lingala, Sajan Goud; Shiroishi, Mark S; Law, Meng; Nayak, Krishna

2016-05-01

To clinically evaluate a highly accelerated T1-weighted dynamic contrast-enhanced (DCE) MRI technique that provides high spatial resolution and whole-brain coverage via undersampling and constrained reconstruction with multiple sparsity constraints. Conventional (rate-2 SENSE) and experimental DCE-MRI (rate-30) scans were performed 20 minutes apart in 15 brain tumor patients. The conventional clinical DCE-MRI had voxel dimensions 0.9 × 1.3 × 7.0 mm(3), FOV 22 × 22 × 4.2 cm(3), and the experimental DCE-MRI had voxel dimensions 0.9 × 0.9 × 1.9 mm(3), and broader coverage 22 × 22 × 19 cm(3). Temporal resolution was 5 s for both protocols. Time-resolved images and blood-brain barrier permeability maps were qualitatively evaluated by two radiologists. The experimental DCE-MRI scans showed no loss of qualitative information in any of the cases, while achieving substantially higher spatial resolution and whole-brain spatial coverage. Average qualitative scores (from 0 to 3) were 2.1 for the experimental scans and 1.1 for the conventional clinical scans. The proposed DCE-MRI approach provides clinically superior image quality with higher spatial resolution and coverage than currently available approaches. These advantages may allow comprehensive permeability mapping in the brain, which is especially valuable in the setting of large lesions or multiple lesions spread throughout the brain.

GRACE Hydrological estimates for small basins: Evaluating processing approaches on the High Plains Aquifer, USA

NASA Astrophysics Data System (ADS)

Longuevergne, Laurent; Scanlon, Bridget R.; Wilson, Clark R.

2010-11-01

The Gravity Recovery and Climate Experiment (GRACE) satellites provide observations of water storage variation at regional scales. However, when focusing on a region of interest, limited spatial resolution and noise contamination can cause estimation bias and spatial leakage, problems that are exacerbated as the region of interest approaches the GRACE resolution limit of a few hundred km. Reliable estimates of water storage variations in small basins require compromises between competing needs for noise suppression and spatial resolution. The objective of this study was to quantitatively investigate processing methods and their impacts on bias, leakage, GRACE noise reduction, and estimated total error, allowing solution of the trade-offs. Among the methods tested is a recently developed concentration algorithm called spatiospectral localization, which optimizes the basin shape description, taking into account limited spatial resolution. This method is particularly suited to retrieval of basin-scale water storage variations and is effective for small basins. To increase confidence in derived methods, water storage variations were calculated for both CSR (Center for Space Research) and GRGS (Groupe de Recherche de Géodésie Spatiale) GRACE products, which employ different processing strategies. The processing techniques were tested on the intensively monitored High Plains Aquifer (450,000 km2 area), where application of the appropriate optimal processing method allowed retrieval of water storage variations over a portion of the aquifer as small as ˜200,000 km2.

Collaborative classification of hyperspectral and visible images with convolutional neural network

NASA Astrophysics Data System (ADS)

Zhang, Mengmeng; Li, Wei; Du, Qian

2017-10-01

Recent advances in remote sensing technology have made multisensor data available for the same area, and it is well-known that remote sensing data processing and analysis often benefit from multisource data fusion. Specifically, low spatial resolution of hyperspectral imagery (HSI) degrades the quality of the subsequent classification task while using visible (VIS) images with high spatial resolution enables high-fidelity spatial analysis. A collaborative classification framework is proposed to fuse HSI and VIS images for finer classification. First, the convolutional neural network model is employed to extract deep spectral features for HSI classification. Second, effective binarized statistical image features are learned as contextual basis vectors for the high-resolution VIS image, followed by a classifier. The proposed approach employs diversified data in a decision fusion, leading to an integration of the rich spectral information, spatial information, and statistical representation information. In particular, the proposed approach eliminates the potential problems of the curse of dimensionality and excessive computation time. The experiments evaluated on two standard data sets demonstrate better classification performance offered by this framework.

Sub-micron resolution selected area electron channeling patterns.

PubMed

Guyon, J; Mansour, H; Gey, N; Crimp, M A; Chalal, S; Maloufi, N

2015-02-01

Collection of selected area channeling patterns (SACPs) on a high resolution FEG-SEM is essential to carry out quantitative electron channeling contrast imaging (ECCI) studies, as it facilitates accurate determination of the crystal plane normal with respect to the incident beam direction and thus allows control the electron channeling conditions. Unfortunately commercial SACP modes developed in the past were limited in spatial resolution and are often no longer offered. In this contribution we present a novel approach for collecting high resolution SACPs (HR-SACPs) developed on a Gemini column. This HR-SACP technique combines the first demonstrated sub-micron spatial resolution with high angular accuracy of about 0.1°, at a convenient working distance of 10mm. This innovative approach integrates the use of aperture alignment coils to rock the beam with a digitally calibrated beam shift procedure to ensure the rocking beam is maintained on a point of interest. Moreover a new methodology to accurately measure SACP spatial resolution is proposed. While column considerations limit the rocking angle to 4°, this range is adequate to index the HR-SACP in conjunction with the pattern simulated from the approximate orientation deduced by EBSD. This new technique facilitates Accurate ECCI (A-ECCI) studies from very fine grained and/or highly strained materials. It offers also new insights for developing HR-SACP modes on new generation high-resolution electron columns. Copyright © 2014 Elsevier B.V. All rights reserved.

Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wood, Andrew W; Leung, Lai R; Sridhar, V

Six approaches for downscaling climate model outputs for use in hydrologic simulation were evaluated, with particular emphasis on each method's ability to produce precipitation and other variables used to drive a macroscale hydrology model applied at much higher spatial resolution than the climate model. Comparisons were made on the basis of a twenty-year retrospective (1975–1995) climate simulation produced by the NCAR-DOE Parallel Climate Model (PCM), and the implications of the comparison for a future (2040–2060) PCM climate scenario were also explored. The six approaches were made up of three relatively simple statistical downscaling methods – linear interpolation (LI), spatial disaggregationmore » (SD), and bias-correction and spatial disaggregation (BCSD) – each applied to both PCM output directly (at T42 spatial resolution), and after dynamical downscaling via a Regional Climate Model (RCM – at ½-degree spatial resolution), for downscaling the climate model outputs to the 1/8-degree spatial resolution of the hydrological model. For the retrospective climate simulation, results were compared to an observed gridded climatology of temperature and precipitation, and gridded hydrologic variables resulting from forcing the hydrologic model with observations. The most significant findings are that the BCSD method was successful in reproducing the main features of the observed hydrometeorology from the retrospective climate simulation, when applied to both PCM and RCM outputs. Linear interpolation produced better results using RCM output than PCM output, but both methods (PCM-LI and RCM-LI) lead to unacceptably biased hydrologic simulations. Spatial disaggregation of the PCM output produced results similar to those achieved with the RCM interpolated output; nonetheless, neither PCM nor RCM output was useful for hydrologic simulation purposes without a bias-correction step. For the future climate scenario, only the BCSD-method (using PCM or RCM) was able to produce hydrologically plausible results. With the BCSD method, the RCM-derived hydrology was more sensitive to climate change than the PCM-derived hydrology.« less

Stretchable Dry Electrodes with Concentric Ring Geometry for Enhancing Spatial Resolution in Electrophysiology.

PubMed

Wang, Kaiping; Parekh, Udit; Pailla, Tejaswy; Garudadri, Harinath; Gilja, Vikash; Ng, Tse Nga

2017-10-01

The multichannel concentric-ring electrodes are stencil printed on stretchable elastomers modified to improve adhesion to skin and minimize motion artifacts for electrophysiological recordings of electroencephalography, electromyography, and electrocardiography. These dry electrodes with a poly(3,4-ethylenedioxythiophene) polystyrene sulfonate interface layer are optimized to show lower noise level than that of commercial gel disc electrodes. The concentric ring geometry enables Laplacian filtering to pinpoint the bioelectric potential source with spatial resolution determined by the ring distance. This work shows a new fabrication approach to integrate and create designs that enhance spatial resolution for high-quality electrophysiology monitoring devices. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Concept for room temperature single-spin tunneling force microscopy with atomic spatial resolution

NASA Astrophysics Data System (ADS)

Payne, Adam

A study of a force detected single-spin magnetic resonance measurement concept with atomic spatial resolution is presented. The method is based upon electrostatic force detection of spin-selection rule controlled single electron tunneling between two electrically isolated paramagnetic states. Single-spin magnetic resonance detection is possible by measuring the force detected tunneling charge noise on and off spin resonance. Simulation results of this charge noise, based upon physical models of the tunneling and spin physics, are directly compared to measured atomic force microscopy (AFM) system noise. The results show that the approach could provide single-spin measurement of electrically isolated defect states with atomic spatial resolution at room temperature.

X-ray simulations method for the large field of view

NASA Astrophysics Data System (ADS)

Schelokov, I. A.; Grigoriev, M. V.; Chukalina, M. V.; Asadchikov, V. E.

2018-03-01

In the standard approach, X-ray simulation is usually limited to the step of spatial sampling to calculate the convolution of integrals of the Fresnel type. Explicitly the sampling step is determined by the size of the last Fresnel zone in the beam aperture. In other words, the spatial sampling is determined by the precision of integral convolution calculations and is not connected with the space resolution of an optical scheme. In the developed approach the convolution in the normal space is replaced by computations of the shear strain of ambiguity function in the phase space. The spatial sampling is then determined by the space resolution of an optical scheme. The sampling step can differ in various directions because of the source anisotropy. The approach was used to simulate original images in the X-ray Talbot interferometry and showed that the simulation can be applied to optimize the methods of postprocessing.

A synchrotron-based local computed tomography combined with data-constrained modelling approach for quantitative analysis of anthracite coal microstructure

PubMed Central

Chen, Wen Hao; Yang, Sam Y. S.; Xiao, Ti Qiao; Mayo, Sherry C.; Wang, Yu Dan; Wang, Hai Peng

2014-01-01

Quantifying three-dimensional spatial distributions of pores and material compositions in samples is a key materials characterization challenge, particularly in samples where compositions are distributed across a range of length scales, and where such compositions have similar X-ray absorption properties, such as in coal. Consequently, obtaining detailed information within sub-regions of a multi-length-scale sample by conventional approaches may not provide the resolution and level of detail one might desire. Herein, an approach for quantitative high-definition determination of material compositions from X-ray local computed tomography combined with a data-constrained modelling method is proposed. The approach is capable of dramatically improving the spatial resolution and enabling finer details within a region of interest of a sample larger than the field of view to be revealed than by using conventional techniques. A coal sample containing distributions of porosity and several mineral compositions is employed to demonstrate the approach. The optimal experimental parameters are pre-analyzed. The quantitative results demonstrated that the approach can reveal significantly finer details of compositional distributions in the sample region of interest. The elevated spatial resolution is crucial for coal-bed methane reservoir evaluation and understanding the transformation of the minerals during coal processing. The method is generic and can be applied for three-dimensional compositional characterization of other materials. PMID:24763649

Hyperspectral Super-Resolution of Locally Low Rank Images From Complementary Multisource Data.

PubMed

Veganzones, Miguel A; Simoes, Miguel; Licciardi, Giorgio; Yokoya, Naoto; Bioucas-Dias, Jose M; Chanussot, Jocelyn

2016-01-01

Remote sensing hyperspectral images (HSIs) are quite often low rank, in the sense that the data belong to a low dimensional subspace/manifold. This has been recently exploited for the fusion of low spatial resolution HSI with high spatial resolution multispectral images in order to obtain super-resolution HSI. Most approaches adopt an unmixing or a matrix factorization perspective. The derived methods have led to state-of-the-art results when the spectral information lies in a low-dimensional subspace/manifold. However, if the subspace/manifold dimensionality spanned by the complete data set is large, i.e., larger than the number of multispectral bands, the performance of these methods mainly decreases because the underlying sparse regression problem is severely ill-posed. In this paper, we propose a local approach to cope with this difficulty. Fundamentally, we exploit the fact that real world HSIs are locally low rank, that is, pixels acquired from a given spatial neighborhood span a very low-dimensional subspace/manifold, i.e., lower or equal than the number of multispectral bands. Thus, we propose to partition the image into patches and solve the data fusion problem independently for each patch. This way, in each patch the subspace/manifold dimensionality is low enough, such that the problem is not ill-posed anymore. We propose two alternative approaches to define the hyperspectral super-resolution through local dictionary learning using endmember induction algorithms. We also explore two alternatives to define the local regions, using sliding windows and binary partition trees. The effectiveness of the proposed approaches is illustrated with synthetic and semi real data.

Single scan parameterization of space-variant point spread functions in image space via a printed array: the impact for two PET/CT scanners.

PubMed

Kotasidis, F A; Matthews, J C; Angelis, G I; Noonan, P J; Jackson, A; Price, P; Lionheart, W R; Reader, A J

2011-05-21

Incorporation of a resolution model during statistical image reconstruction often produces images of improved resolution and signal-to-noise ratio. A novel and practical methodology to rapidly and accurately determine the overall emission and detection blurring component of the system matrix using a printed point source array within a custom-made Perspex phantom is presented. The array was scanned at different positions and orientations within the field of view (FOV) to examine the feasibility of extrapolating the measured point source blurring to other locations in the FOV and the robustness of measurements from a single point source array scan. We measured the spatially-variant image-based blurring on two PET/CT scanners, the B-Hi-Rez and the TruePoint TrueV. These measured spatially-variant kernels and the spatially-invariant kernel at the FOV centre were then incorporated within an ordinary Poisson ordered subset expectation maximization (OP-OSEM) algorithm and compared to the manufacturer's implementation using projection space resolution modelling (RM). Comparisons were based on a point source array, the NEMA IEC image quality phantom, the Cologne resolution phantom and two clinical studies (carbon-11 labelled anti-sense oligonucleotide [(11)C]-ASO and fluorine-18 labelled fluoro-l-thymidine [(18)F]-FLT). Robust and accurate measurements of spatially-variant image blurring were successfully obtained from a single scan. Spatially-variant resolution modelling resulted in notable resolution improvements away from the centre of the FOV. Comparison between spatially-variant image-space methods and the projection-space approach (the first such report, using a range of studies) demonstrated very similar performance with our image-based implementation producing slightly better contrast recovery (CR) for the same level of image roughness (IR). These results demonstrate that image-based resolution modelling within reconstruction is a valid alternative to projection-based modelling, and that, when using the proposed practical methodology, the necessary resolution measurements can be obtained from a single scan. This approach avoids the relatively time-consuming and involved procedures previously proposed in the literature.

Improving urban land use and land cover classification from high-spatial-resolution hyperspectral imagery using contextual information

NASA Astrophysics Data System (ADS)

Yang, He; Ma, Ben; Du, Qian; Yang, Chenghai

2010-08-01

In this paper, we propose approaches to improve the pixel-based support vector machine (SVM) classification for urban land use and land cover (LULC) mapping from airborne hyperspectral imagery with high spatial resolution. Class spatial neighborhood relationship is used to correct the misclassified class pairs, such as roof and trail, road and roof. These classes may be difficult to be separated because they may have similar spectral signatures and their spatial features are not distinct enough to help their discrimination. In addition, misclassification incurred from within-class trivial spectral variation can be corrected by using pixel connectivity information in a local window so that spectrally homogeneous regions can be well preserved. Our experimental results demonstrate the efficiency of the proposed approaches in classification accuracy improvement. The overall performance is competitive to the object-based SVM classification.

Magnetoacoustic Imaging of Electrical Conductivity of Biological Tissues at a Spatial Resolution Better than 2 mm

PubMed Central

Hu, Gang; He, Bin

2011-01-01

Magnetoacoustic tomography with magnetic induction (MAT-MI) is an emerging approach for noninvasively imaging electrical impedance properties of biological tissues. The MAT-MI imaging system measures ultrasound waves generated by the Lorentz force, having been induced by magnetic stimulation, which is related to the electrical conductivity distribution in tissue samples. MAT-MI promises to provide fine spatial resolution for biological tissue imaging as compared to ultrasound resolution. In the present study, we first estimated the imaging spatial resolution by calculating the full width at half maximum (FWHM) of the system point spread function (PSF). The actual spatial resolution of our MAT-MI system was experimentally determined to be 1.51 mm by a parallel-line-source phantom with Rayleigh criterion. Reconstructed images made from tissue-mimicking gel phantoms, as well as animal tissue samples, were consistent with the morphological structures of the samples. The electrical conductivity value of the samples was determined directly by a calibrated four-electrode system. It has been demonstrated that MAT-MI is able to image the electrical impedance properties of biological tissues with better than 2 mm spatial resolution. These results suggest the potential of MAT-MI for application to early detection of small-size diseased tissues (e.g. small breast cancer). PMID:21858111

High spatial resolution diffusion weighted imaging on clinical 3 T MRI scanners using multislab spiral acquisitions

PubMed Central

Holtrop, Joseph L.; Sutton, Bradley P.

2016-01-01

Abstract. A diffusion weighted imaging (DWI) approach that is signal-to-noise ratio (SNR) efficient and can be applied to achieve sub-mm resolutions on clinical 3 T systems was developed. The sequence combined a multislab, multishot pulsed gradient spin echo diffusion scheme with spiral readouts for imaging data and navigators. Long data readouts were used to keep the number of shots, and hence total imaging time, for the three-dimensional acquisition short. Image quality was maintained by incorporating a field-inhomogeneity-corrected image reconstruction to remove distortions associated with long data readouts. Additionally, multiple shots were required for the high-resolution images, necessitating motion induced phase correction through the use of efficiently integrated navigator data. The proposed approach is compared with two-dimensional (2-D) acquisitions that use either a spiral or a typical echo-planar imaging (EPI) acquisition to demonstrate the improved SNR efficiency. The proposed technique provided 71% higher SNR efficiency than the standard 2-D EPI approach. The adaptability of the technique to achieve high spatial resolutions is demonstrated by acquiring diffusion tensor imaging data sets with isotropic resolutions of 1.25 and 0.8 mm. The proposed approach allows for SNR-efficient sub-mm acquisitions of DWI data on clinical 3 T systems. PMID:27088107

Propagation phasor approach for holographic image reconstruction

PubMed Central

Luo, Wei; Zhang, Yibo; Göröcs, Zoltán; Feizi, Alborz; Ozcan, Aydogan

2016-01-01

To achieve high-resolution and wide field-of-view, digital holographic imaging techniques need to tackle two major challenges: phase recovery and spatial undersampling. Previously, these challenges were separately addressed using phase retrieval and pixel super-resolution algorithms, which utilize the diversity of different imaging parameters. Although existing holographic imaging methods can achieve large space-bandwidth-products by performing pixel super-resolution and phase retrieval sequentially, they require large amounts of data, which might be a limitation in high-speed or cost-effective imaging applications. Here we report a propagation phasor approach, which for the first time combines phase retrieval and pixel super-resolution into a unified mathematical framework and enables the synthesis of new holographic image reconstruction methods with significantly improved data efficiency. In this approach, twin image and spatial aliasing signals, along with other digital artifacts, are interpreted as noise terms that are modulated by phasors that analytically depend on the lateral displacement between hologram and sensor planes, sample-to-sensor distance, wavelength, and the illumination angle. Compared to previous holographic reconstruction techniques, this new framework results in five- to seven-fold reduced number of raw measurements, while still achieving a competitive resolution and space-bandwidth-product. We also demonstrated the success of this approach by imaging biological specimens including Papanicolaou and blood smears. PMID:26964671

A Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI)

NASA Astrophysics Data System (ADS)

Houborg, Rasmus; McCabe, Matthew F.; Gao, Feng

2016-05-01

Satellite remote sensing has been used successfully to map leaf area index (LAI) across landscapes, but advances are still needed to exploit multi-scale data streams for producing LAI at both high spatial and temporal resolution. A multi-scale Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI) has been developed to generate 4-day time-series of Landsat-scale LAI from existing medium resolution LAI products. STEM-LAI has been designed to meet the demands of applications requiring frequent and spatially explicit information, such as effectively resolving rapidly evolving vegetation dynamics at sub-field (30 m) scales. In this study, STEM-LAI is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) based LAI data and utilizes a reference-based regression tree approach for producing MODIS-consistent, but Landsat-based, LAI. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is used to interpolate the downscaled LAI between Landsat acquisition dates, providing a high spatial and temporal resolution improvement over existing LAI products. STARFM predicts high resolution LAI by blending MODIS and Landsat based information from a common acquisition date, with MODIS data from a prediction date. To demonstrate its capacity to reproduce fine-scale spatial features observed in actual Landsat LAI, the STEM-LAI approach is tested over an agricultural region in Nebraska. The implementation of a 250 m resolution LAI product, derived from MODIS 1 km data and using a scale consistent approach based on the Normalized Difference Vegetation Index (NDVI), is found to significantly improve accuracies of spatial pattern prediction, with the coefficient of efficiency (E) ranging from 0.77-0.94 compared to 0.01-0.85 when using 1 km LAI inputs alone. Comparisons against an 11-year record of in-situ measured LAI over maize and soybean highlight the utility of STEM-LAI in reproducing observed LAI dynamics (both characterized by r2 = 0.86) over a range of plant development stages. Overall, STEM-LAI represents an effective downscaling and temporal enhancement mechanism that predicts in-situ measured LAI better than estimates derived through linear interpolation between Landsat acquisitions. This is particularly true when the in-situ measurement date is greater than 10 days from the nearest Landsat acquisition, with prediction errors reduced by up to 50%. With a streamlined and completely automated processing interface, STEM-LAI represents a flexible tool for LAI disaggregation in space and time that is adaptable to different land cover types, landscape heterogeneities, and cloud cover conditions.

Exploring the Potential of PROBA-V for Evapotranspiration Monitoring in Wetlands

NASA Astrophysics Data System (ADS)

Barrios, Jose Miguel; Ghilain, Nicolas; Arboleda, Alirio; Gellens-Meulenberghs, Francoise

2016-08-01

This study aims at deriving daily evapotranspiration (ET) estimates at a convenient spatial resolution for ecosystem monitoring. The methodological approach was based on the computation of the energy balance over the study sites. The study explored the potential of integrating remote sensing (RS) products derived from the Meteosat Second Generation (MSG) satellite -in virtue of their high temporal resolution- and Proba-V data, supplying moderate spatial resolution data. This strategy was tested for the year 2014 on three wetlands sites located in Europe where eddy covariance measurements were available for validation. The modelled results correlated well with the validation data and showed the added value of combining the strengths of different satellite missions. The results open interesting perspectives for refining this approach with the upcoming Sentinel-3 datasets.

Example-Based Super-Resolution Fluorescence Microscopy.

PubMed

Jia, Shu; Han, Boran; Kutz, J Nathan

2018-04-23

Capturing biological dynamics with high spatiotemporal resolution demands the advancement in imaging technologies. Super-resolution fluorescence microscopy offers spatial resolution surpassing the diffraction limit to resolve near-molecular-level details. While various strategies have been reported to improve the temporal resolution of super-resolution imaging, all super-resolution techniques are still fundamentally limited by the trade-off associated with the longer image acquisition time that is needed to achieve higher spatial information. Here, we demonstrated an example-based, computational method that aims to obtain super-resolution images using conventional imaging without increasing the imaging time. With a low-resolution image input, the method provides an estimate of its super-resolution image based on an example database that contains super- and low-resolution image pairs of biological structures of interest. The computational imaging of cellular microtubules agrees approximately with the experimental super-resolution STORM results. This new approach may offer potential improvements in temporal resolution for experimental super-resolution fluorescence microscopy and provide a new path for large-data aided biomedical imaging.

Spatial Heterogeneity of Leaf Area Index (LAI) and Its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA).

PubMed

Reichenau, Tim G; Korres, Wolfgang; Montzka, Carsten; Fiener, Peter; Wilken, Florian; Stadler, Anja; Waldhoff, Guido; Schneider, Karl

2016-01-01

The ratio of leaf area to ground area (leaf area index, LAI) is an important state variable in ecosystem studies since it influences fluxes of matter and energy between the land surface and the atmosphere. As a basis for generating temporally continuous and spatially distributed datasets of LAI, the current study contributes an analysis of its spatial variability and spatial structure. Soil-vegetation-atmosphere fluxes of water, carbon and energy are nonlinearly related to LAI. Therefore, its spatial heterogeneity, i.e., the combination of spatial variability and structure, has an effect on simulations of these fluxes. To assess LAI spatial heterogeneity, we apply a Comprehensive Data Analysis Approach that combines data from remote sensing (5 m resolution) and simulation (150 m resolution) with field measurements and a detailed land use map. Test area is the arable land in the fertile loess plain of the Rur catchment on the Germany-Belgium-Netherlands border. LAI from remote sensing and simulation compares well with field measurements. Based on the simulation results, we describe characteristic crop-specific temporal patterns of LAI spatial variability. By means of these patterns, we explain the complex multimodal frequency distributions of LAI in the remote sensing data. In the test area, variability between agricultural fields is higher than within fields. Therefore, spatial resolutions less than the 5 m of the remote sensing scenes are sufficient to infer LAI spatial variability. Frequency distributions from the simulation agree better with the multimodal distributions from remote sensing than normal distributions do. The spatial structure of LAI in the test area is dominated by a short distance referring to field sizes. Longer distances that refer to soil and weather can only be derived from remote sensing data. Therefore, simulations alone are not sufficient to characterize LAI spatial structure. It can be concluded that a comprehensive picture of LAI spatial heterogeneity and its temporal course can contribute to the development of an approach to create spatially distributed and temporally continuous datasets of LAI.

Spatial Heterogeneity of Leaf Area Index (LAI) and Its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA)

PubMed Central

Korres, Wolfgang; Montzka, Carsten; Fiener, Peter; Wilken, Florian; Stadler, Anja; Waldhoff, Guido; Schneider, Karl

2016-01-01

The ratio of leaf area to ground area (leaf area index, LAI) is an important state variable in ecosystem studies since it influences fluxes of matter and energy between the land surface and the atmosphere. As a basis for generating temporally continuous and spatially distributed datasets of LAI, the current study contributes an analysis of its spatial variability and spatial structure. Soil-vegetation-atmosphere fluxes of water, carbon and energy are nonlinearly related to LAI. Therefore, its spatial heterogeneity, i.e., the combination of spatial variability and structure, has an effect on simulations of these fluxes. To assess LAI spatial heterogeneity, we apply a Comprehensive Data Analysis Approach that combines data from remote sensing (5 m resolution) and simulation (150 m resolution) with field measurements and a detailed land use map. Test area is the arable land in the fertile loess plain of the Rur catchment on the Germany-Belgium-Netherlands border. LAI from remote sensing and simulation compares well with field measurements. Based on the simulation results, we describe characteristic crop-specific temporal patterns of LAI spatial variability. By means of these patterns, we explain the complex multimodal frequency distributions of LAI in the remote sensing data. In the test area, variability between agricultural fields is higher than within fields. Therefore, spatial resolutions less than the 5 m of the remote sensing scenes are sufficient to infer LAI spatial variability. Frequency distributions from the simulation agree better with the multimodal distributions from remote sensing than normal distributions do. The spatial structure of LAI in the test area is dominated by a short distance referring to field sizes. Longer distances that refer to soil and weather can only be derived from remote sensing data. Therefore, simulations alone are not sufficient to characterize LAI spatial structure. It can be concluded that a comprehensive picture of LAI spatial heterogeneity and its temporal course can contribute to the development of an approach to create spatially distributed and temporally continuous datasets of LAI. PMID:27391858

Using Aerosol Reflectance for Dust Detection

NASA Astrophysics Data System (ADS)

Bahramvash Shams, S.; Mohammadzade, A.

2013-09-01

In this study we propose an approach for dust detection by aerosol reflectance over arid and urban region in clear sky condition. In urban and arid areas surface reflectance in red and infrared spectral is bright and hence shorter wavelength is required for this detections. Main step of our approach can be mentioned as: cloud mask for excluding cloudy pixels from our calculation, calculate Rayleigh path radiance, construct a surface reflectance data base, estimate aerosol reflectance, detect dust aerosol, dust detection and evaluations of dust detection. Spectral with wavelength 0.66, 0.55, 0.47 μm has been used in our dust detection. Estimating surface reflectance is the most challenging step of obtaining aerosol reflectance from top of atmosphere (TOA) reflectance. Hence for surface estimation we had created a surface reflectance database of 0.05 degree latitude by 0.05 degree longitude resolution by using minimum reflectivity technique (MRT). In order to evaluate our dust detection algorithm MODIS aerosol product MOD04 and common dust detection method named Brightness Temperature Difference (BTD) had been used. We had implemented this method to Moderate Resolution Imaging Spectroradiometer (MODIS) image of part of Iran (7 degree latitude and 8 degree longitude) spring 2005 dust phenomenon from April to June. This study uses MODIS LIB calibrated reflectance high spatial resolution (500 m) MOD02Hkm on TERRA spacecraft. Hence our dust detection spatial resolution will be higher spatial resolution than MODIS aerosol product MOD04 which has 10 × 10 km2 and BTD resolution is 1 km due to the band 29 (8.7 μm), 31 (11 μm), and 32 (12 μm) spatial resolutions.

Spatially resolved δ13C analysis using laser ablation isotope ratio mass spectrometry

NASA Astrophysics Data System (ADS)

Moran, J.; Riha, K. M.; Nims, M. K.; Linley, T. J.; Hess, N. J.; Nico, P. S.

2014-12-01

Inherent geochemical, organic matter, and microbial heterogeneity over small spatial scales can complicate studies of carbon dynamics through soils. Stable isotope analysis has a strong history of helping track substrate turnover, delineate rhizosphere activity zones, and identifying transitions in vegetation cover, but most traditional isotope approaches are limited in spatial resolution by a combination of physical separation techniques (manual dissection) and IRMS instrument sensitivity. We coupled laser ablation sampling with isotope measurement via IRMS to enable spatially resolved analysis over solid surfaces. Once a targeted sample region is ablated the resulting particulates are entrained in a helium carrier gas and passed through a combustion reactor where carbon is converted to CO2. Cyrotrapping of the resulting CO2 enables a reduction in carrier gas flow which improves overall measurement sensitivity versus traditional, high flow sample introduction. Currently we are performing sample analysis at 50 μm resolution, require 65 ng C per analysis, and achieve measurement precision consistent with other continuous flow techniques. We will discuss applications of the laser ablation IRMS (LA-IRMS) system to microbial communities and fish ecology studies to demonstrate the merits of this technique and how similar analytical approaches can be transitioned to soil systems. Preliminary efforts at analyzing soil samples will be used to highlight strengths and limitations of the LA-IRMS approach, paying particular attention to sample preparation requirements, spatial resolution, sample analysis time, and the types of questions most conducive to analysis via LA-IRMS.

Evaluation of multiple-scale 3D characterization for coal physical structure with DCM method and synchrotron X-ray CT.

PubMed

Wang, Haipeng; Yang, Yushuang; Yang, Jianli; Nie, Yihang; Jia, Jing; Wang, Yudan

2015-01-01

Multiscale nondestructive characterization of coal microscopic physical structure can provide important information for coal conversion and coal-bed methane extraction. In this study, the physical structure of a coal sample was investigated by synchrotron-based multiple-energy X-ray CT at three beam energies and two different spatial resolutions. A data-constrained modeling (DCM) approach was used to quantitatively characterize the multiscale compositional distributions at the two resolutions. The volume fractions of each voxel for four different composition groups were obtained at the two resolutions. Between the two resolutions, the difference for DCM computed volume fractions of coal matrix and pores is less than 0.3%, and the difference for mineral composition groups is less than 0.17%. This demonstrates that the DCM approach can account for compositions beyond the X-ray CT imaging resolution with adequate accuracy. By using DCM, it is possible to characterize a relatively large coal sample at a relatively low spatial resolution with minimal loss of the effect due to subpixel fine length scale structures.

High resolution MR based polymer dosimetry versus film densitometry: a systematic study based on the modulation transfer function approach.

PubMed

Berg, A; Pernkopf, M; Waldhäusl, C; Schmidt, W; Moser, E

2004-09-07

Precise methods of modem radiation therapy such as intensity modulated radiotherapy (IMRT), brachytherapy (BT) and high LET irradiation allow for high dose localization in volumes of a few mm3. However, most dosimetry methods-ionization chambers, TLD arrangements or silicon detectors, for example-are not capable of detecting sub-mm dose variations or do not allow for simple dose imaging. Magnetic resonance based polymer dosimetry (MRPD) appears to be well suited to three-dimensional high resolution relative dosimetry but the spatial resolution based on a systematic modulation transfer function (MTF) approach has not yet been investigated. We offer a theoretical construct for addressing the spatial resolution in different dose imaging systems, i.e. the dose modulation transfer function (DMTF) approach, an experimental realization of this concept with a phantom and quantitative comparisons between two dosimetric systems: polymer gel and film dosimetry. Polymer gel samples were irradiated by Co-60 photons through an absorber grid which is characterized by periodic structures of different spatial period (a), the smallest one at width of a/2 = 280 microm. The modulation in dose under the grid is visualized via calibrated, high resolution, parameter-selective (T2) and dose images based on multi-echo MR imaging. The DMTF is obtained from the modulation depth of the spin-spin relaxation time (T2) after calibration. Voxel sizes below 0.04 mm3 could be achieved, which are significantly smaller than those reported in MR based dose imaging on polymer gels elsewhere, using a powerful gradient system and a highly sensitive small birdcage resonator on a whole-body 3T MR scanner. Dose modulations at 22% of maximum dose amplitude could be observed at about 2 line pairs per mm. The polymer DMTF results are compared to those of a typical clinical film-scanner system. This study demonstrates that MR based gel dosimetry at 200 microm pixel resolution might even be superior, with reference to relative spatial resolution, to the results of a standard film-scanner system offering a nominal scan resolution of 200 microm.

Spatial Modeling and Uncertainty Assessment of Fine Scale Surface Processes Based on Coarse Terrain Elevation Data

NASA Astrophysics Data System (ADS)

Rasera, L. G.; Mariethoz, G.; Lane, S. N.

2017-12-01

Frequent acquisition of high-resolution digital elevation models (HR-DEMs) over large areas is expensive and difficult. Satellite-derived low-resolution digital elevation models (LR-DEMs) provide extensive coverage of Earth's surface but at coarser spatial and temporal resolutions. Although useful for large scale problems, LR-DEMs are not suitable for modeling hydrologic and geomorphic processes at scales smaller than their spatial resolution. In this work, we present a multiple-point geostatistical approach for downscaling a target LR-DEM based on available high-resolution training data and recurrent high-resolution remote sensing images. The method aims at generating several equiprobable HR-DEMs conditioned to a given target LR-DEM by borrowing small scale topographic patterns from an analogue containing data at both coarse and fine scales. An application of the methodology is demonstrated by using an ensemble of simulated HR-DEMs as input to a flow-routing algorithm. The proposed framework enables a probabilistic assessment of the spatial structures generated by natural phenomena operating at scales finer than the available terrain elevation measurements. A case study in the Swiss Alps is provided to illustrate the methodology.

High-resolution whole-brain DCE-MRI using constrained reconstruction: Prospective clinical evaluation in brain tumor patients

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guo, Yi, E-mail: yiguo@usc.edu; Zhu, Yinghua; Lingala, Sajan Goud

Purpose: To clinically evaluate a highly accelerated T1-weighted dynamic contrast-enhanced (DCE) MRI technique that provides high spatial resolution and whole-brain coverage via undersampling and constrained reconstruction with multiple sparsity constraints. Methods: Conventional (rate-2 SENSE) and experimental DCE-MRI (rate-30) scans were performed 20 minutes apart in 15 brain tumor patients. The conventional clinical DCE-MRI had voxel dimensions 0.9 × 1.3 × 7.0 mm{sup 3}, FOV 22 × 22 × 4.2 cm{sup 3}, and the experimental DCE-MRI had voxel dimensions 0.9 × 0.9 × 1.9 mm{sup 3}, and broader coverage 22 × 22 × 19 cm{sup 3}. Temporal resolution was 5 smore » for both protocols. Time-resolved images and blood–brain barrier permeability maps were qualitatively evaluated by two radiologists. Results: The experimental DCE-MRI scans showed no loss of qualitative information in any of the cases, while achieving substantially higher spatial resolution and whole-brain spatial coverage. Average qualitative scores (from 0 to 3) were 2.1 for the experimental scans and 1.1 for the conventional clinical scans. Conclusions: The proposed DCE-MRI approach provides clinically superior image quality with higher spatial resolution and coverage than currently available approaches. These advantages may allow comprehensive permeability mapping in the brain, which is especially valuable in the setting of large lesions or multiple lesions spread throughout the brain.« less

Advancing the quantification of humid tropical forest cover loss with multi-resolution optical remote sensing data: Sampling & wall-to-wall mapping

NASA Astrophysics Data System (ADS)

Broich, Mark

Humid tropical forest cover loss is threatening the sustainability of ecosystem goods and services as vast forest areas are rapidly cleared for industrial scale agriculture and tree plantations. Despite the importance of humid tropical forest in the provision of ecosystem services and economic development opportunities, the spatial and temporal distribution of forest cover loss across large areas is not well quantified. Here I improve the quantification of humid tropical forest cover loss using two remote sensing-based methods: sampling and wall-to-wall mapping. In all of the presented studies, the integration of coarse spatial, high temporal resolution data with moderate spatial, low temporal resolution data enable advances in quantifying forest cover loss in the humid tropics. Imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used as the source of coarse spatial resolution, high temporal resolution data and imagery from the Landsat Enhanced Thematic Mapper Plus (ETM+) sensor are used as the source of moderate spatial, low temporal resolution data. In a first study, I compare the precision of different sampling designs for the Brazilian Amazon using the annual deforestation maps derived by the Brazilian Space Agency for reference. I show that sampling designs can provide reliable deforestation estimates; furthermore, sampling designs guided by MODIS data can provide more efficient estimates than the systematic design used for the United Nations Food and Agricultural Organization Forest Resource Assessment 2010. Sampling approaches, such as the one demonstrated, are viable in regions where data limitations, such as cloud contamination, limit exhaustive mapping methods. Cloud-contaminated regions experiencing high rates of change include Insular Southeast Asia, specifically Indonesia and Malaysia. Due to persistent cloud cover, forest cover loss in Indonesia has only been mapped at a 5-10 year interval using photo interpretation of single best Landsat images. Such an approach does not provide timely results, and cloud cover reduces the utility of map outputs. In a second study, I develop a method to exhaustively mine the recently opened Landsat archive for cloud-free observations and automatically map forest cover loss for Sumatra and Kalimantan for the 2000-2005 interval. In a comparison with a reference dataset consisting of 64 Landsat sample blocks, I show that my method, using per pixel time-series, provides more accurate forest cover loss maps for multiyear intervals than approaches using image composites. In a third study, I disaggregate Landsat-mapped forest cover loss, mapped over a multiyear interval, by year using annual forest cover loss maps generated from coarse spatial, high temporal resolution MODIS imagery. I further disaggregate and analyze forest cover loss by forest land use, and provinces. Forest cover loss trends show high spatial and temporal variability. These results underline the importance of annual mapping for the quantification of forest cover loss in Indonesia, specifically in the light of the developing Reducing Emissions from Deforestation and Forest Degradation in Developing Countries policy framework (REDD). All three studies highlight the advances in quantifying forest cover loss in the humid tropics made by integrating coarse spatial, high temporal resolution data with moderate spatial, low temporal resolution data. The three methods presented can be combined into an integrated monitoring strategy.

Characterization of dynamic changes of current source localization based on spatiotemporal fMRI constrained EEG source imaging

NASA Astrophysics Data System (ADS)

Nguyen, Thinh; Potter, Thomas; Grossman, Robert; Zhang, Yingchun

2018-06-01

Objective. Neuroimaging has been employed as a promising approach to advance our understanding of brain networks in both basic and clinical neuroscience. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) represent two neuroimaging modalities with complementary features; EEG has high temporal resolution and low spatial resolution while fMRI has high spatial resolution and low temporal resolution. Multimodal EEG inverse methods have attempted to capitalize on these properties but have been subjected to localization error. The dynamic brain transition network (DBTN) approach, a spatiotemporal fMRI constrained EEG source imaging method, has recently been developed to address these issues by solving the EEG inverse problem in a Bayesian framework, utilizing fMRI priors in a spatial and temporal variant manner. This paper presents a computer simulation study to provide a detailed characterization of the spatial and temporal accuracy of the DBTN method. Approach. Synthetic EEG data were generated in a series of computer simulations, designed to represent realistic and complex brain activity at superficial and deep sources with highly dynamical activity time-courses. The source reconstruction performance of the DBTN method was tested against the fMRI-constrained minimum norm estimates algorithm (fMRIMNE). The performances of the two inverse methods were evaluated both in terms of spatial and temporal accuracy. Main results. In comparison with the commonly used fMRIMNE method, results showed that the DBTN method produces results with increased spatial and temporal accuracy. The DBTN method also demonstrated the capability to reduce crosstalk in the reconstructed cortical time-course(s) induced by neighboring regions, mitigate depth bias and improve overall localization accuracy. Significance. The improved spatiotemporal accuracy of the reconstruction allows for an improved characterization of complex neural activity. This improvement can be extended to any subsequent brain connectivity analyses used to construct the associated dynamic brain networks.

High-spatial resolution multispectral and panchromatic satellite imagery for mapping perennial desert plants

NASA Astrophysics Data System (ADS)

Alsharrah, Saad A.; Bruce, David A.; Bouabid, Rachid; Somenahalli, Sekhar; Corcoran, Paul A.

2015-10-01

The use of remote sensing techniques to extract vegetation cover information for the assessment and monitoring of land degradation in arid environments has gained increased interest in recent years. However, such a task can be challenging, especially for medium-spatial resolution satellite sensors, due to soil background effects and the distribution and structure of perennial desert vegetation. In this study, we utilised Pleiades high-spatial resolution, multispectral (2m) and panchromatic (0.5m) imagery and focused on mapping small shrubs and low-lying trees using three classification techniques: 1) vegetation indices (VI) threshold analysis, 2) pre-built object-oriented image analysis (OBIA), and 3) a developed vegetation shadow model (VSM). We evaluated the success of each approach using a root of the sum of the squares (RSS) metric, which incorporated field data as control and three error metrics relating to commission, omission, and percent cover. Results showed that optimum VI performers returned good vegetation cover estimates at certain thresholds, but failed to accurately map the distribution of the desert plants. Using the pre-built IMAGINE Objective OBIA approach, we improved the vegetation distribution mapping accuracy, but this came at the cost of over classification, similar to results of lowering VI thresholds. We further introduced the VSM which takes into account shadow for further refining vegetation cover classification derived from VI. The results showed significant improvements in vegetation cover and distribution accuracy compared to the other techniques. We argue that the VSM approach using high-spatial resolution imagery provides a more accurate representation of desert landscape vegetation and should be considered in assessments of desertification.

Towards high temporal and moderate spatial resolutions in the remote sensing retrieval of evapotranspiration by combining geostationary and polar orbit satellite data

NASA Astrophysics Data System (ADS)

Barrios, José Miguel; Ghilain, Nicolas; Arboleda, Alirio; Gellens-Meulenberghs, Françoise

2014-05-01

Evapotranspiration (ET) is the water flux going from the surface into the atmosphere as result of soil and surface water evaporation and plant transpiration. It constitutes a key component of the water cycle and its quantification is of crucial importance for a number of applications like water management, climatic modelling, agriculture monitoring and planning, etc. Estimating ET is not an easy task; specially if large areas are envisaged and various spatio-temporal patterns of ET are present as result of heterogeneity in land cover, land use and climatic conditions. In this respect, spaceborne remote sensing (RS) provides the only alternative to continuously measure surface parameters related to ET over large areas. The Royal Meteorological Institute (RMI) of Belgium, in the framework of EUMETSAT's "Land Surface Analysis-Satellite Application Facility" (LSA-SAF), has developed a model for the estimation of ET. The model is forced by RS data, numerical weather predictions and land cover information. The RS forcing is derived from measurements by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellite. This ET model is operational and delivers ET estimations over the whole field of view of the MSG satellite (Europe, Africa and Eastern South America) (http://landsaf.meteo.pt) every 30 minutes. The spatial resolution of MSG is 3 x 3 km at subsatellite point and about 4 x 5 km in continental Europe. The spatial resolution of this product may constrain its full exploitation as the interest of potential users (farmers and natural resources scientists) may lie on smaller spatial units. This study aimed at testing methodological alternatives to combine RS imagery (geostationary and polar orbit satellites) for the estimation of ET such that the spatial resolution of the final product is improved. In particular, the study consisted in the implementation of two approaches for combining the current ET estimations with RS data containing information over vegetation parameters and captured by polar orbit spaceborne sensors. The first tested approach consisted in forcing the operational ET algorithm with RS measurements obtained from a moderate spatial resolution sensor. The variables with improved spatial resolution were leaf area index and albedo. Other variables of the model remained unchanged with respect to the operational version. In the second approach, a two phases procedure was implemented. Firstly, a preliminary approximation of ET was obtained as a function of solar radiation, air temperature and a vegetation index. The value was then statistically adjusted on the basis of the ET estimations by the operational algorithm. The results of implementing the different approaches were tested against eddy covariance ET derived from measurements in Fluxnet towers spread across Europe and representing different landscape characteristics. The analysis allowed the identification of pros and cons of the tested methodological approaches as well as their performance in different land cover arrangements.

Reconstruction of a Three Hourly 1-km Land Surface Air Temperature Dataset in the Qinghai-Tibet Plateau

NASA Astrophysics Data System (ADS)

Zhou, J.; Ding, L.

2017-12-01

Land surface air temperature (SAT) is an important parameter in the modeling of radiation balance and energy budget of the earth surface. Generally, SAT is measured at ground meteorological stations; then SAT mapping is possible though a spatial interpolation process. The interpolated SAT map relies on the spatial distribution of ground stations, the terrain, and many other factors; thus, it has great uncertainties in regions with complicated terrain. Instead, SAT map can also be obtained through physical modeling of interactions between the land surface and the atmosphere. Such dataset generally has coarse spatial resolution (e.g. coarser than 0.1°) and cannot satisfy the applications at fine scales, e.g. 1 km. This presentation reports the reconstruction of a three hourly 1-km SAT dataset from 2001 to 2015 over the Qinghai-Tibet Plateau. The terrain in the Qinghai-Tibet Plateau, especially in the eastern part, is extremely complicated. Two SAT datasets with good qualities are used in this study. The first one is from the 3h China Meteorological Forcing Dataset with a 0.1° resolution released by the Institute of Tibetan Plateau Research, Chinese Academy of Sciences (Yang et al., 2010); the second one is from the ERA-Interim product with the same temporal resolution and a 0.125° resolution. A statistical approach is developed to downscale the spatial resolution of the derived SAT to 1-km. The elevation and the normalized difference vegetation index (NDVI) are selected as two scaling factors in the downscaling approach. Results demonstrate there is significantly negative correlation between the SAT and elevation in all seasons; there is also significantly negative correlation between the SAT and NDVI in the vegetation growth seasons, while the correlation decreases in the other seasons. Therefore, a temporally dynamic downscaling approach is feasible to enhance the spatial resolution of the SAT. Compared with the SAT at the 0.1° or 0.125°, the reconstructed 1-km SAT can provide much more spatial details in areas with complicated terrain. Additionally, the 1-km SAT agrees well with the ground measured air temperatures as well as the SAT before downscaling. The reconstructed SAT will be beneficial for the modeling of surface radiation balance and energy budget over the Qinghai-Tibet Plateau.

Highly-resolved Modeling of Emissions and Concentrations of Carbon Monoxide, Carbon Dioxide, Nitrogen Oxides, and Fine Particulate Matter in Salt Lake City, Utah

NASA Astrophysics Data System (ADS)

Mendoza, D. L.; Lin, J. C.; Mitchell, L.; Ehleringer, J. R.

2014-12-01

Accurate, high-resolution data on air pollutant emissions and concentrations are needed to understand human exposures and for both policy and pollutant management purposes. An important step in this process is also quantification of uncertainties. We present a spatially explicit and highly resolved emissions inventory for Salt Lake County, Utah, and trace gas concentration estimates for carbon monoxide (CO), carbon dioxide (CO2), nitrogen oxides (NOx) and fine particles (PM2.5) within Salt Lake City. We assess the validity of this approach by comparing measured concentrations against simulated values derived from combining the emissions inventory with an atmospheric model. The emissions inventory for the criteria pollutants was constructed using the 2011 National Emissions Inventory (NEI). The spatial and temporal allocation methods from the Emission Modeling Clearinghouse data set are used to downscale the NEI data from annual to hourly scales and from county-level to 500 m x 500 m resolution. Onroad mobile source emissions were estimated by combining a bottom-up emissions calculation approach for large roadway links with a top-down spatial allocation approach for other roadways. Vehicle activity data for road links were derived from automatic traffic responder data. The emissions inventory for CO2 was obtained from the Hestia emissions data product at an hourly, building, facility, and road link resolution. The AERMOD and CALPUFF dispersion models were used to transport emissions and estimate air pollutant concentrations at an hourly temporal and 500 m x 500 m spatial resolution. Modeled results were compared against measurements from a mobile lab equipped with trace gas measurement equipment traveling on pre-determined routes in the Salt Lake City area. The comparison between both approaches to concentration estimation highlights spatial locations and hours of high variability/uncertainty. Results presented here will inform understanding of variability and uncertainty in emissions and concentrations to better inform future policy. This work will also facilitate the development of a systematic approach to incorporate measurement data and models to better inform estimates of pollutant concentrations that determine the extent to which urban populations are exposed to adverse air quality.

Attenuated total reflection-Fourier transform infrared imaging of large areas using inverted prism crystals and combining imaging and mapping.

PubMed

Chan, K L Andrew; Kazarian, Sergei G

2008-10-01

Attenuated total reflection-Fourier transform infrared (ATR-FT-IR) imaging is a very useful tool for capturing chemical images of various materials due to the simple sample preparation and the ability to measure wet samples or samples in an aqueous environment. However, the size of the array detector used for image acquisition is often limited and there is usually a trade off between spatial resolution and the field of view (FOV). The combination of mapping and imaging can be used to acquire images with a larger FOV without sacrificing spatial resolution. Previous attempts have demonstrated this using an infrared microscope and a Germanium hemispherical ATR crystal to achieve images of up to 2.5 mm x 2.5 mm but with varying spatial resolution and depth of penetration across the imaged area. In this paper, we demonstrate a combination of mapping and imaging with a different approach using an external optics housing for large ATR accessories and inverted ATR prisms to achieve ATR-FT-IR images with a large FOV and reasonable spatial resolution. The results have shown that a FOV of 10 mm x 14 mm can be obtained with a spatial resolution of approximately 40-60 microm when using an accessory that gives no magnification. A FOV of 1.3 mm x 1.3 mm can be obtained with spatial resolution of approximately 15-20 microm when using a diamond ATR imaging accessory with 4x magnification. No significant change in image quality such as spatial resolution or depth of penetration has been observed across the whole FOV with this method and the measurement time was approximately 15 minutes for an image consisting of 16 image tiles.

Light-sheet enhanced resolution of light field microscopy for rapid imaging of large volumes

NASA Astrophysics Data System (ADS)

Madrid Wolff, Jorge; Castro, Diego; Arbeláez, Pablo; Forero-Shelton, Manu

2018-02-01

Whole-brain imaging is challenging because it demands microscopes with high temporal and spatial resolution, which are often at odds, especially in the context of large fields of view. We have designed and built a light-sheet microscope with digital micromirror illumination and light-field detection. On the one hand, light sheets provide high resolution optical sectioning on live samples without compromising their viability. On the other hand, light field imaging makes it possible to reconstruct full volumes of relatively large fields of view from a single camera exposure; however, its enhanced temporal resolution comes at the expense of spatial resolution, limiting its applicability. We present an approach to increase the resolution of light field images using DMD-based light sheet illumination. To that end, we develop a method to produce synthetic resolution targets for light field microscopy and a procedure to correct the depth at which planes are refocused with rendering software. We measured the axial resolution as a function of depth and show a three-fold potential improvement with structured illumination, albeit by sacrificing some temporal resolution, also three-fold. This results in an imaging system that may be adjusted to specific needs without having to reassemble and realign it. This approach could be used to image relatively large samples at high rates.

Added-values of high spatiotemporal remote sensing data in crop yield estimation

NASA Astrophysics Data System (ADS)

Gao, F.; Anderson, M. C.

2017-12-01

Timely and accurate estimation of crop yield before harvest is critical for food market and administrative planning. Remote sensing derived parameters have been used for estimating crop yield by using either empirical or crop growth models. The uses of remote sensing vegetation index (VI) in crop yield modeling have been typically evaluated at regional and country scales using coarse spatial resolution (a few hundred to kilo-meters) data or assessed over a small region at field level using moderate resolution spatial resolution data (10-100m). Both data sources have shown great potential in capturing spatial and temporal variability in crop yield. However, the added value of data with both high spatial and temporal resolution data has not been evaluated due to the lack of such data source with routine, global coverage. In recent years, more moderate resolution data have become freely available and data fusion approaches that combine data acquired from different spatial and temporal resolutions have been developed. These make the monitoring crop condition and estimating crop yield at field scale become possible. Here we investigate the added value of the high spatial and temporal VI for describing variability of crop yield. The explanatory ability of crop yield based on high spatial and temporal resolution remote sensing data was evaluated in a rain-fed agricultural area in the U.S. Corn Belt. Results show that the fused Landsat-MODIS (high spatial and temporal) VI explains yield variability better than single data source (Landsat or MODIS alone), with EVI2 performing slightly better than NDVI. The maximum VI describes yield variability better than cumulative VI. Even though VI is effective in explaining yield variability within season, the inter-annual variability is more complex and need additional information (e.g. weather, water use and management). Our findings augment the importance of high spatiotemporal remote sensing data and supports new moderate resolution satellite missions for agricultural applications.

Adaptive zooming in X-ray computed tomography.

PubMed

Dabravolski, Andrei; Batenburg, Kees Joost; Sijbers, Jan

2014-01-01

In computed tomography (CT), the source-detector system commonly rotates around the object in a circular trajectory. Such a trajectory does not allow to exploit a detector fully when scanning elongated objects. Increase the spatial resolution of the reconstructed image by optimal zooming during scanning. A new approach is proposed, in which the full width of the detector is exploited for every projection angle. This approach is based on the use of prior information about the object's convex hull to move the source as close as possible to the object, while avoiding truncation of the projections. Experiments show that the proposed approach can significantly improve reconstruction quality, producing reconstructions with smaller errors and revealing more details in the object. The proposed approach can lead to more accurate reconstructions and increased spatial resolution in the object compared to the conventional circular trajectory.

Resolution Analysis of finite fault inversions: A back-projection approach.

NASA Astrophysics Data System (ADS)

Ji, C.; Shao, G.

2007-12-01

The resolution of inverted source models of large earthquakes is controlled by frequency contents of "coherent" (or "useful") seismic observations and their spatial distribution. But it is difficult to distinguish whether some features consistent during different inversions are really required by data or a consequence of "prior" information, such as velocity structures, fault geometry, model parameterizations. Here, we investigate the model spatial resolution by first back projecting and stacking the data at the source regions and then analyzing the spatial- temporal variations of the focusing regions, which arbitrarily defined as the regions with 90% of the peak focusing amplitude. Our preliminary results indicated 1) The spatial-temporal resolution at a particularly direction is controlled by the region of directivity parameter [pcos(θ)] within the seismic network, where p is the horizontal slowness from the hypocenter and θ is the difference between the station azimuth and this orientation. Therefore, the network aperture is more important than the number of stations. 2) Simple stacking method is a robust method to capture the asperities but the sizes of focusing regions are usually much larger than what data could resolve. By carefully weighting the data before the stacking could enhance the spatial resolution in a particular direction. 3) The results based on the teleseismic P waves of a local network usually surfers the trade-off between the source's spatial location and its rupture time. The resolution of the 2001 Kunlunshan earthquake and 2006 Kuril island earthquake will be investigated.

Resolution improvement in positron emission tomography using anatomical Magnetic Resonance Imaging.

PubMed

Chu, Yong; Su, Min-Ying; Mandelkern, Mark; Nalcioglu, Orhan

2006-08-01

An ideal imaging system should provide information with high-sensitivity, high spatial, and temporal resolution. Unfortunately, it is not possible to satisfy all of these desired features in a single modality. In this paper, we discuss methods to improve the spatial resolution in positron emission imaging (PET) using a priori information from Magnetic Resonance Imaging (MRI). Our approach uses an image restoration algorithm based on the maximization of mutual information (MMI), which has found significant success for optimizing multimodal image registration. The MMI criterion is used to estimate the parameters in the Sharpness-Constrained Wiener filter. The generated filter is then applied to restore PET images of a realistic digital brain phantom. The resulting restored images show improved resolution and better signal-to-noise ratio compared to the interpolated PET images. We conclude that a Sharpness-Constrained Wiener filter having parameters optimized from a MMI criterion may be useful for restoring spatial resolution in PET based on a priori information from correlated MRI.

Optimized AVHRR land surface temperature downscaling method for local scale observations: case study for the coastal area of the Gulf of Gdańsk

NASA Astrophysics Data System (ADS)

Chybicki, Andrzej; Łubniewski, Zbigniew

2017-09-01

Satellite imaging systems have known limitations regarding their spatial and temporal resolution. The approaches based on subpixel mapping of the Earth's environment, which rely on combining the data retrieved from sensors of higher temporal and lower spatial resolution with the data characterized by lower temporal but higher spatial resolution, are of considerable interest. The paper presents the downscaling process of the land surface temperature (LST) derived from low resolution imagery acquired by the Advanced Very High Resolution Radiometer (AVHRR), using the inverse technique. The effective emissivity derived from another data source is used as a quantity describing thermal properties of the terrain in higher resolution, and allows the downsampling of low spatial resolution LST images. The authors propose an optimized downscaling method formulated as the inverse problem and show that the proposed approach yields better results than the use of other downsampling methods. The proposed method aims to find estimation of high spatial resolution LST data by minimizing the global error of the downscaling. In particular, for the investigated region of the Gulf of Gdansk, the RMSE between the AVHRR image downscaled by the proposed method and the Landsat 8 LST reference image was 2.255°C with correlation coefficient R equal to 0.828 and Bias = 0.557°C. For comparison, using the PBIM method, it was obtained RMSE = 2.832°C, R = 0.775 and Bias = 0.997°C for the same satellite scene. It also has been shown that the obtained results are also good in local scale and can be used for areas much smaller than the entire satellite imagery scene, depicting diverse biophysical conditions. Specifically, for the analyzed set of small sub-datasets of the whole scene, the obtained RSME between the downscaled and reference image was smaller, by approx. 0.53°C on average, in the case of applying the proposed method than in the case of using the PBIM method.

High resolution quantitative phase imaging of live cells with constrained optimization approach

NASA Astrophysics Data System (ADS)

Pandiyan, Vimal Prabhu; Khare, Kedar; John, Renu

2016-03-01

Quantitative phase imaging (QPI) aims at studying weakly scattering and absorbing biological specimens with subwavelength accuracy without any external staining mechanisms. Use of a reference beam at an angle is one of the necessary criteria for recording of high resolution holograms in most of the interferometric methods used for quantitative phase imaging. The spatial separation of the dc and twin images is decided by the reference beam angle and Fourier-filtered reconstructed image will have a very poor resolution if hologram is recorded below a minimum reference angle condition. However, it is always inconvenient to have a large reference beam angle while performing high resolution microscopy of live cells and biological specimens with nanometric features. In this paper, we treat reconstruction of digital holographic microscopy images as a constrained optimization problem with smoothness constraint in order to recover only complex object field in hologram plane even with overlapping dc and twin image terms. We solve this optimization problem by gradient descent approach iteratively and the smoothness constraint is implemented by spatial averaging with appropriate size. This approach will give excellent high resolution image recovery compared to Fourier filtering while keeping a very small reference angle. We demonstrate this approach on digital holographic microscopy of live cells by recovering the quantitative phase of live cells from a hologram recorded with nearly zero reference angle.

Micromirror structured illumination microscope for high-speed in vivo drosophila brain imaging.

PubMed

Masson, A; Pedrazzani, M; Benrezzak, S; Tchenio, P; Preat, T; Nutarelli, D

2014-01-27

Genetic tools and especially genetically encoded fluorescent reporters have given a special place to optical microscopy in drosophila neurobiology research. In order to monitor neural networks activity, high speed and sensitive techniques, with high spatial resolution are required. Structured illumination microscopies are wide-field approaches with optical sectioning ability. Despite the large progress made with the introduction of the HiLo principle, they did not meet the criteria of speed and/or spatial resolution for drosophila brain imaging. We report on a new implementation that took advantage of micromirror matrix technology to structure the illumination. Thus, we showed that the developed instrument exhibits a spatial resolution close to that of confocal microscopy but it can record physiological responses with a speed improved by more than an order a magnitude.

Microsphere-aided optical microscopy and its applications for super-resolution imaging

NASA Astrophysics Data System (ADS)

Upputuri, Paul Kumar; Pramanik, Manojit

2017-12-01

The spatial resolution of a standard optical microscope (SOM) is limited by diffraction. In visible spectrum, SOM can provide ∼ 200 nm resolution. To break the diffraction limit several approaches were developed including scanning near field microscopy, metamaterial super-lenses, nanoscale solid immersion lenses, super-oscillatory lenses, confocal fluorescence microscopy, techniques that exploit non-linear response of fluorophores like stimulated emission depletion microscopy, stochastic optical reconstruction microscopy, etc. Recently, photonic nanojet generated by a dielectric microsphere was used to break the diffraction limit. The microsphere-approach is simple, cost-effective and can be implemented under a standard microscope, hence it has gained enormous attention for super-resolution imaging. In this article, we briefly review the microsphere approach and its applications for super-resolution imaging in various optical imaging modalities.

GIEMS-D3: A new long-term, dynamical, high-spatial resolution inundation extent dataset at global scale

NASA Astrophysics Data System (ADS)

Aires, Filipe; Miolane, Léo; Prigent, Catherine; Pham Duc, Binh; Papa, Fabrice; Fluet-Chouinard, Etienne; Lehner, Bernhard

2017-04-01

The Global Inundation Extent from Multi-Satellites (GIEMS) provides multi-year monthly variations of the global surface water extent at 25kmx25km resolution. It is derived from multiple satellite observations. Its spatial resolution is usually compatible with climate model outputs and with global land surface model grids but is clearly not adequate for local applications that require the characterization of small individual water bodies. There is today a strong demand for high-resolution inundation extent datasets, for a large variety of applications such as water management, regional hydrological modeling, or for the analysis of mosquitos-related diseases. A new procedure is introduced to downscale the GIEMS low spatial resolution inundations to a 3 arc second (90 m) dataset. The methodology is based on topography and hydrography information from the HydroSHEDS database. A new floodability index is adopted and an innovative smoothing procedure is developed to ensure the smooth transition, in the high-resolution maps, between the low-resolution boxes from GIEMS. Topography information is relevant for natural hydrology environments controlled by elevation, but is more limited in human-modified basins. However, the proposed downscaling approach is compatible with forthcoming fusion with other more pertinent satellite information in these difficult regions. The resulting GIEMS-D3 database is the only high spatial resolution inundation database available globally at the monthly time scale over the 1993-2007 period. GIEMS-D3 is assessed by analyzing its spatial and temporal variability, and evaluated by comparisons to other independent satellite observations from visible (Google Earth and Landsat), infrared (MODIS) and active microwave (SAR).

Super-resolution mapping using multi-viewing CHRIS/PROBA data

NASA Astrophysics Data System (ADS)

Dwivedi, Manish; Kumar, Vinay

2016-04-01

High-spatial resolution Remote Sensing (RS) data provides detailed information which ensures high-definition visual image analysis of earth surface features. These data sets also support improved information extraction capabilities at a fine scale. In order to improve the spatial resolution of coarser resolution RS data, the Super Resolution Reconstruction (SRR) technique has become widely acknowledged which focused on multi-angular image sequences. In this study multi-angle CHRIS/PROBA data of Kutch area is used for SR image reconstruction to enhance the spatial resolution from 18 m to 6m in the hope to obtain a better land cover classification. Various SR approaches like Projection onto Convex Sets (POCS), Robust, Iterative Back Projection (IBP), Non-Uniform Interpolation and Structure-Adaptive Normalized Convolution (SANC) chosen for this study. Subjective assessment through visual interpretation shows substantial improvement in land cover details. Quantitative measures including peak signal to noise ratio and structural similarity are used for the evaluation of the image quality. It was observed that SANC SR technique using Vandewalle algorithm for the low resolution image registration outperformed the other techniques. After that SVM based classifier is used for the classification of SRR and data resampled to 6m spatial resolution using bi-cubic interpolation. A comparative analysis is carried out between classified data of bicubic interpolated and SR derived images of CHRIS/PROBA and SR derived classified data have shown a significant improvement of 10-12% in the overall accuracy. The results demonstrated that SR methods is able to improve spatial detail of multi-angle images as well as the classification accuracy.

Generating high temporal and spatial resolution thermal band imagery using robust sharpening approach

USDA-ARS?s Scientific Manuscript database

Thermal infrared band imagery provides key information for detecting wild fires, mapping land surface energy fluxes and evapotranspiration, monitoring urban heat fluxes and drought monitoring. Thermal infrared (TIR) imagery at fine resolution is required for field scale applications. However, therma...

Correcting the effect of refraction and dispersion of light in FT-IR spectroscopic imaging in transmission through thick infrared windows.

PubMed

Chan, K L Andrew; Kazarian, Sergei G

2013-01-15

Transmission mode is one of the most common sampling methods for FT-IR spectroscopic imaging because the spectra obtained generally have a reasonable signal-to-noise ratio. However, dispersion and refraction of infrared light occurs when samples are sandwiched between infrared windows or placed underneath a layer of liquid. Dispersion and refraction cause infrared light to focus with different focal lengths depending on the wavelength (wavenumber) of the light. As a result, images obtained are in focus only at a particular wavenumber while they are defocused at other wavenumber values. In this work, a solution to correct this spread of focus by means of adding a lens on top of the infrared transparent window, such that a pseudo hemisphere is formed, has been investigated. Through this lens (or pseudo hemisphere), refraction of light is removed and the light across the spectral range has the same focal depth. Furthermore, the lens acts as a solid immersion objective and an increase of both magnification and spatial resolution (by 1.4 times) is demonstrated. The spatial resolution was investigated using an USAF resolution target, showing that the Rayleigh criterion can be achieved, as well as a sample with a sharp polymer interface to indicate the spatial resolution that can be expected in real samples. The reported approach was used to obtain chemical images of cross sections of cancer tissue and hair samples sandwiched between infrared windows showing the versatility and applicability of the method. In addition to the improved spatial resolution, the results reported herein also demonstrate that the lens can reduce the effect of scattering near the edges of tissue samples. The advantages of the presented approach, obtaining FT-IR spectroscopic images in transmission mode with the same focus across all wavenumber values and simultaneous improvement in spatial resolution, will have wide implications ranging from studies of live cells to sorption of drugs into tissues.

Theoretical limit of spatial resolution in diffuse optical tomography using a perturbation model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Konovalov, A B; Vlasov, V V

2014-03-28

We have assessed the limit of spatial resolution of timedomain diffuse optical tomography (DOT) based on a perturbation reconstruction model. From the viewpoint of the structure reconstruction accuracy, three different approaches to solving the inverse DOT problem are compared. The first approach involves reconstruction of diffuse tomograms from straight lines, the second – from average curvilinear trajectories of photons and the third – from total banana-shaped distributions of photon trajectories. In order to obtain estimates of resolution, we have derived analytical expressions for the point spread function and modulation transfer function, as well as have performed a numerical experiment onmore » reconstruction of rectangular scattering objects with circular absorbing inhomogeneities. It is shown that in passing from reconstruction from straight lines to reconstruction using distributions of photon trajectories we can improve resolution by almost an order of magnitude and exceed the accuracy of reconstruction of multi-step algorithms used in DOT. (optical tomography)« less

Markov-random-field-based super-resolution mapping for identification of urban trees in VHR images

NASA Astrophysics Data System (ADS)

Ardila, Juan P.; Tolpekin, Valentyn A.; Bijker, Wietske; Stein, Alfred

2011-11-01

Identification of tree crowns from remote sensing requires detailed spectral information and submeter spatial resolution imagery. Traditional pixel-based classification techniques do not fully exploit the spatial and spectral characteristics of remote sensing datasets. We propose a contextual and probabilistic method for detection of tree crowns in urban areas using a Markov random field based super resolution mapping (SRM) approach in very high resolution images. Our method defines an objective energy function in terms of the conditional probabilities of panchromatic and multispectral images and it locally optimizes the labeling of tree crown pixels. Energy and model parameter values are estimated from multiple implementations of SRM in tuning areas and the method is applied in QuickBird images to produce a 0.6 m tree crown map in a city of The Netherlands. The SRM output shows an identification rate of 66% and commission and omission errors in small trees and shrub areas. The method outperforms tree crown identification results obtained with maximum likelihood, support vector machines and SRM at nominal resolution (2.4 m) approaches.

ICA-based artefact and accelerated fMRI acquisition for improved Resting State Network imaging

PubMed Central

Griffanti, Ludovica; Salimi-Khorshidi, Gholamreza; Beckmann, Christian F.; Auerbach, Edward J.; Douaud, Gwenaëlle; Sexton, Claire E.; Zsoldos, Enikő; Ebmeier, Klaus P; Filippini, Nicola; Mackay, Clare E.; Moeller, Steen; Xu, Junqian; Yacoub, Essa; Baselli, Giuseppe; Ugurbil, Kamil; Miller, Karla L.; Smith, Stephen M.

2014-01-01

The identification of resting state networks (RSNs) and the quantification of their functional connectivity in resting-state fMRI (rfMRI) are seriously hindered by the presence of artefacts, many of which overlap spatially or spectrally with RSNs. Moreover, recent developments in fMRI acquisition yield data with higher spatial and temporal resolutions, but may increase artefacts both spatially and/or temporally. Hence the correct identification and removal of non-neural fluctuations is crucial, especially in accelerated acquisitions. In this paper we investigate the effectiveness of three data-driven cleaning procedures, compare standard against higher (spatial and temporal) resolution accelerated fMRI acquisitions, and investigate the combined effect of different acquisitions and different cleanup approaches. We applied single-subject independent component analysis (ICA), followed by automatic component classification with FMRIB’s ICA-based X-noiseifier (FIX) to identify artefactual components. We then compared two first-level (within-subject) cleaning approaches for removing those artefacts and motion-related fluctuations from the data. The effectiveness of the cleaning procedures were assessed using timeseries (amplitude and spectra), network matrix and spatial map analyses. For timeseries and network analyses we also tested the effect of a second-level cleaning (informed by group-level analysis). Comparing these approaches, the preferable balance between noise removal and signal loss was achieved by regressing out of the data the full space of motion-related fluctuations and only the unique variance of the artefactual ICA components. Using similar analyses, we also investigated the effects of different cleaning approaches on data from different acquisition sequences. With the optimal cleaning procedures, functional connectivity results from accelerated data were statistically comparable or significantly better than the standard (unaccelerated) acquisition, and, crucially, with higher spatial and temporal resolution. Moreover, we were able to perform higher dimensionality ICA decompositions with the accelerated data, which is very valuable for detailed network analyses. PMID:24657355

ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging.

PubMed

Griffanti, Ludovica; Salimi-Khorshidi, Gholamreza; Beckmann, Christian F; Auerbach, Edward J; Douaud, Gwenaëlle; Sexton, Claire E; Zsoldos, Enikő; Ebmeier, Klaus P; Filippini, Nicola; Mackay, Clare E; Moeller, Steen; Xu, Junqian; Yacoub, Essa; Baselli, Giuseppe; Ugurbil, Kamil; Miller, Karla L; Smith, Stephen M

2014-07-15

The identification of resting state networks (RSNs) and the quantification of their functional connectivity in resting-state fMRI (rfMRI) are seriously hindered by the presence of artefacts, many of which overlap spatially or spectrally with RSNs. Moreover, recent developments in fMRI acquisition yield data with higher spatial and temporal resolutions, but may increase artefacts both spatially and/or temporally. Hence the correct identification and removal of non-neural fluctuations is crucial, especially in accelerated acquisitions. In this paper we investigate the effectiveness of three data-driven cleaning procedures, compare standard against higher (spatial and temporal) resolution accelerated fMRI acquisitions, and investigate the combined effect of different acquisitions and different cleanup approaches. We applied single-subject independent component analysis (ICA), followed by automatic component classification with FMRIB's ICA-based X-noiseifier (FIX) to identify artefactual components. We then compared two first-level (within-subject) cleaning approaches for removing those artefacts and motion-related fluctuations from the data. The effectiveness of the cleaning procedures was assessed using time series (amplitude and spectra), network matrix and spatial map analyses. For time series and network analyses we also tested the effect of a second-level cleaning (informed by group-level analysis). Comparing these approaches, the preferable balance between noise removal and signal loss was achieved by regressing out of the data the full space of motion-related fluctuations and only the unique variance of the artefactual ICA components. Using similar analyses, we also investigated the effects of different cleaning approaches on data from different acquisition sequences. With the optimal cleaning procedures, functional connectivity results from accelerated data were statistically comparable or significantly better than the standard (unaccelerated) acquisition, and, crucially, with higher spatial and temporal resolution. Moreover, we were able to perform higher dimensionality ICA decompositions with the accelerated data, which is very valuable for detailed network analyses. Copyright © 2014 Elsevier Inc. All rights reserved.

A scintillator geometry suitable for very small PET gantries

NASA Astrophysics Data System (ADS)

Gonzalez, A. J.; Gonzalez-Montoro, A.; Aguilar, A.; Cañizares, G.; Martí, R.; Iranzo, S.; Lamprou, E.; Sanchez, S.; Sanchez, F.; Benlloch, J. M.

2017-12-01

In this work we are describing a novel approach to the scintillator crystal configuration as used in nuclear medicine imaging. Our design is related to the coupling in one PET module of the two separate crystal configurations used so far there: monolithic and crystal arrays. The particular design we have studied is based on a two-layer scintillator approach (hybrid) composed of a monolithic LYSO crystal (5-6 mm thickness) and a LYSO crystal array with 4-5 mm height (0.8 and 1 mm pixels). We show here the detector block performance, in terms of spatial, energy and DOI information, to be used as a module in the design of PET scanners. The design we propose allows one to achieve accurate three-dimensional spatial resolution (including DOI information) while assuring high detection efficiency at reasonable cost. Moreover, the proposed design improves the spatial response uniformity across the whole detector module, and especially at the edge region. The crystal arrays are mounted in the front and were well resolved. The monolithic crystal inserted between crystal array and the photosensor, provided measured FWHM resolution as good as 1.5-1.7 mm including the 1 mm source size. The monolithic block achieved a DOI resolution (FWHM) nearing 3 mm. We compared these results with an approach in which we use a single monolithic block with total volume equals to the hybrid approach. In general, comparable performances were obtained.

Accounting for small scale heterogeneity in ecohydrologic watershed models

NASA Astrophysics Data System (ADS)

Bhaskar, A.; Fleming, B.; Hogan, D. M.

2016-12-01

Spatially distributed ecohydrologic models are inherently constrained by the spatial resolution of their smallest units, below which land and processes are assumed to be homogenous. At coarse scales, heterogeneity is often accounted for by computing store and fluxes of interest over a distribution of land cover types (or other sources of heterogeneity) within spatially explicit modeling units. However this approach ignores spatial organization and the lateral transfer of water and materials downslope. The challenge is to account both for the role of flow network topology and fine-scale heterogeneity. We present a new approach that defines two levels of spatial aggregation and that integrates spatially explicit network approach with a flexible representation of finer-scale aspatial heterogeneity. Critically, this solution does not simply increase the resolution of the smallest spatial unit, and so by comparison, results in improved computational efficiency. The approach is demonstrated by adapting Regional Hydro-Ecologic Simulation System (RHESSys), an ecohydrologic model widely used to simulate climate, land use, and land management impacts. We illustrate the utility of our approach by showing how the model can be used to better characterize forest thinning impacts on ecohydrology. Forest thinning is typically done at the scale of individual trees, and yet management responses of interest include impacts on watershed scale hydrology and on downslope riparian vegetation. Our approach allow us to characterize the variability in tree size/carbon reduction and water transfers between neighboring trees while still capturing hillslope to watershed scale effects, Our illustrative example demonstrates that accounting for these fine scale effects can substantially alter model estimates, in some cases shifting the impacts of thinning on downslope water availability from increases to decreases. We conclude by describing other use cases that may benefit from this approach including characterizing urban vegetation and storm water management features and their impact on watershed scale hydrology and biogeochemical cycling.

Accounting for small scale heterogeneity in ecohydrologic watershed models

NASA Astrophysics Data System (ADS)

Burke, W.; Tague, C.

2017-12-01

Spatially distributed ecohydrologic models are inherently constrained by the spatial resolution of their smallest units, below which land and processes are assumed to be homogenous. At coarse scales, heterogeneity is often accounted for by computing store and fluxes of interest over a distribution of land cover types (or other sources of heterogeneity) within spatially explicit modeling units. However this approach ignores spatial organization and the lateral transfer of water and materials downslope. The challenge is to account both for the role of flow network topology and fine-scale heterogeneity. We present a new approach that defines two levels of spatial aggregation and that integrates spatially explicit network approach with a flexible representation of finer-scale aspatial heterogeneity. Critically, this solution does not simply increase the resolution of the smallest spatial unit, and so by comparison, results in improved computational efficiency. The approach is demonstrated by adapting Regional Hydro-Ecologic Simulation System (RHESSys), an ecohydrologic model widely used to simulate climate, land use, and land management impacts. We illustrate the utility of our approach by showing how the model can be used to better characterize forest thinning impacts on ecohydrology. Forest thinning is typically done at the scale of individual trees, and yet management responses of interest include impacts on watershed scale hydrology and on downslope riparian vegetation. Our approach allow us to characterize the variability in tree size/carbon reduction and water transfers between neighboring trees while still capturing hillslope to watershed scale effects, Our illustrative example demonstrates that accounting for these fine scale effects can substantially alter model estimates, in some cases shifting the impacts of thinning on downslope water availability from increases to decreases. We conclude by describing other use cases that may benefit from this approach including characterizing urban vegetation and storm water management features and their impact on watershed scale hydrology and biogeochemical cycling.

Towards a 3d Spatial Urban Energy Modelling Approach

NASA Astrophysics Data System (ADS)

Bahu, J.-M.; Koch, A.; Kremers, E.; Murshed, S. M.

2013-09-01

Today's needs to reduce the environmental impact of energy use impose dramatic changes for energy infrastructure and existing demand patterns (e.g. buildings) corresponding to their specific context. In addition, future energy systems are expected to integrate a considerable share of fluctuating power sources and equally a high share of distributed generation of electricity. Energy system models capable of describing such future systems and allowing the simulation of the impact of these developments thus require a spatial representation in order to reflect the local context and the boundary conditions. This paper describes two recent research approaches developed at EIFER in the fields of (a) geo-localised simulation of heat energy demand in cities based on 3D morphological data and (b) spatially explicit Agent-Based Models (ABM) for the simulation of smart grids. 3D city models were used to assess solar potential and heat energy demand of residential buildings which enable cities to target the building refurbishment potentials. Distributed energy systems require innovative modelling techniques where individual components are represented and can interact. With this approach, several smart grid demonstrators were simulated, where heterogeneous models are spatially represented. Coupling 3D geodata with energy system ABMs holds different advantages for both approaches. On one hand, energy system models can be enhanced with high resolution data from 3D city models and their semantic relations. Furthermore, they allow for spatial analysis and visualisation of the results, with emphasis on spatially and structurally correlations among the different layers (e.g. infrastructure, buildings, administrative zones) to provide an integrated approach. On the other hand, 3D models can benefit from more detailed system description of energy infrastructure, representing dynamic phenomena and high resolution models for energy use at component level. The proposed modelling strategies conceptually and practically integrate urban spatial and energy planning approaches. The combined modelling approach that will be developed based on the described sectorial models holds the potential to represent hybrid energy systems coupling distributed generation of electricity with thermal conversion systems.

Land cover mapping and change detection in urban watersheds using QuickBird high spatial resolution satellite imagery

NASA Astrophysics Data System (ADS)

Hester, David Barry

The objective of this research was to develop methods for urban land cover analysis using QuickBird high spatial resolution satellite imagery. Such imagery has emerged as a rich commercially available remote sensing data source and has enjoyed high-profile broadcast news media and Internet applications, but methods of quantitative analysis have not been thoroughly explored. The research described here consists of three studies focused on the use of pan-sharpened 61-cm spatial resolution QuickBird imagery, the spatial resolution of which is the highest of any commercial satellite. In the first study, a per-pixel land cover classification method is developed for use with this imagery. This method utilizes a per-pixel classification approach to generate an accurate six-category high spatial resolution land cover map of a developing suburban area. The primary objective of the second study was to develop an accurate land cover change detection method for use with QuickBird land cover products. This work presents an efficient fuzzy framework for transforming map uncertainty into accurate and meaningful high spatial resolution land cover change analysis. The third study described here is an urban planning application of the high spatial resolution QuickBird-based land cover product developed in the first study. This work both meaningfully connects this exciting new data source to urban watershed management and makes an important empirical contribution to the study of suburban watersheds. Its analysis of residential roads and driveways as well as retail parking lots sheds valuable light on the impact of transportation-related land use on the suburban landscape. Broadly, these studies provide new methods for using state-of-the-art remote sensing data to inform land cover analysis and urban planning. These methods are widely adaptable and produce land cover products that are both meaningful and accurate. As additional high spatial resolution satellites are launched and the cost of high resolution imagery continues to decline, this research makes an important contribution to this exciting era in the science of remote sensing.

Building block extraction and classification by means of aerial images fused with super-resolution reconstructed elevation data

NASA Astrophysics Data System (ADS)

Panagiotopoulou, Antigoni; Bratsolis, Emmanuel; Charou, Eleni; Perantonis, Stavros

2017-10-01

The detailed three-dimensional modeling of buildings utilizing elevation data, such as those provided by light detection and ranging (LiDAR) airborne scanners, is increasingly demanded today. There are certain application requirements and available datasets to which any research effort has to be adapted. Our dataset includes aerial orthophotos, with a spatial resolution 20 cm, and a digital surface model generated from LiDAR, with a spatial resolution 1 m and an elevation resolution 20 cm, from an area of Athens, Greece. The aerial images are fused with LiDAR, and we classify these data with a multilayer feedforward neural network for building block extraction. The innovation of our approach lies in the preprocessing step in which the original LiDAR data are super-resolution (SR) reconstructed by means of a stochastic regularized technique before their fusion with the aerial images takes place. The Lorentzian estimator combined with the bilateral total variation regularization performs the SR reconstruction. We evaluate the performance of our approach against that of fusing unprocessed LiDAR data with aerial images. We present the classified images and the statistical measures confusion matrix, kappa coefficient, and overall accuracy. The results demonstrate that our approach predominates over that of fusing unprocessed LiDAR data with aerial images.

An automated tool for a daily harmful algal bloom monitoring using MODIS imagery downscaled to 250 meters spatial resolution

NASA Astrophysics Data System (ADS)

El Alem, A.

2016-12-01

Harmful algal bloom (HAB) causes negative impacts to other organisms by producing natural toxins, mechanical damage to other micro-organisms, or simply by degrading waters quality. Contaminated waters could expose several billions of population to serious intoxications problems. Traditionally, HAB monitoring is made with standard methods limited to a restricted network of sampling points. However, rapid evolution of HABs makes it difficult to monitor their variation in time and space, threating then public safety. Daily monitoring is then the best way to control and to mitigate their harmful effect upon population, particularly for sources feeding cities. Recently, an approach for estimating chlorophyll-a (Chl-a) concentration, as a proxy of HAB presence, in inland waters based MODIS imagery downscaled to 250 meters spatial resolution was developed. Statistical evaluation of the developed approach highlighted the accuracy of Chl-a estimate with a R2 = 0.98, a relative RMSE of 15%, a relative BIAS of -2%, and a relative NASH of 0.95. Temporal resolution of MODIS sensor allows then a daily monitoring of HAB spatial distribution for inland waters of more than 2.25 Km2 of surface. Groupe-Hemisphere, a company specialized in environmental and sustainable planning in Quebec, has shown a great interest to the developed approach. Given the complexity of the preprocessing (geometric and atmospheric corrections as well as downscaling spatial resolution) and processing (Chl-a estimate) of images, a standalone application under the MATLAB's GUI environment was developed. The application allows an automated process for all preprocessing and processing steps. Outputs produced by the application for end users, many of whom may be decision makers or policy makers in the public and private sectors, allows a near-real time monitoring of water quality for a more efficient management.

Elevated-temperature luminescence measurements to improve spatial resolution

NASA Astrophysics Data System (ADS)

Pluska, Mariusz; Czerwinski, Andrzej

2018-01-01

Various branches of applied physics use luminescence based methods to investigate light-emitting specimens with high spatial resolution. A key problem is that luminescence signals lack all the advantages of high locality (i.e. of high spatial resolution) when structures with strong built-in electric field are measured. Such fields exist intentionally in most photonic structures, and occur unintentionally in many other materials. In this case, as a result of beam-induced current generation and its outflow, information that indicates irregularities, nonuniformities and inhomogeneities, such as defects, is lost. We show that to avoid nonlocality and enable truly local luminescence measurements, an elevated measurement temperature as high as 350 K (or even higher) is, perhaps surprisingly, advantageous. This is in contrast to a widely used approach, where cryogenic temperatures, or at least room temperature, are recommended. The elevated temperature of a specimen, together with the current outflow being limited by focused ion beam (FIB) milling, is shown to improve the spatial resolution of luminescence measurements greatly. All conclusions drawn using the example of cathodoluminescence are useful for other luminescence techniques.

Improved wetland classification using eight-band high-resolution satellite imagery and a hybrid approach

EPA Science Inventory

Although remote sensing technology has long been used in wetland inventory and monitoring, the accuracy and detail level of derived wetland maps were limited or often unsatisfactory largely due to the relatively coarse spatial resolution of conventional satellite imagery. This re...

Application of spatial pedotransfer functions to understand soil modulation of vegetation response to climate

USDA-ARS?s Scientific Manuscript database

A fundamental knowledge gap in understanding land-atmosphere interactions is accurate, high resolution spatial representation of soil physical and hydraulic properties. We present a novel approach to predict hydraulic soil parameters by combining digital soil mapping techniques with pedotransfer fun...

Generating daily high spatial land surface temperatures by combining ASTER and MODIS land surface temperature products for environmental process monitoring.

PubMed

Wu, Mingquan; Li, Hua; Huang, Wenjiang; Niu, Zheng; Wang, Changyao

2015-08-01

There is a shortage of daily high spatial land surface temperature (LST) data for use in high spatial and temporal resolution environmental process monitoring. To address this shortage, this work used the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), and the Spatial and Temporal Data Fusion Approach (STDFA) to estimate high spatial and temporal resolution LST by combining Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST and Moderate Resolution Imaging Spectroradiometer (MODIS) LST products. The actual ASTER LST products were used to evaluate the precision of the combined LST images using the correlation analysis method. This method was tested and validated in study areas located in Gansu Province, China. The results show that all the models can generate daily synthetic LST image with a high correlation coefficient (r) of 0.92 between the synthetic image and the actual ASTER LST observations. The ESTARFM has the best performance, followed by the STDFA and the STARFM. Those models had better performance in desert areas than in cropland. The STDFA had better noise immunity than the other two models.

Transparent, Flexible, Low Noise Graphene Electrodes for Simultaneous Electrophysiology and Neuroimaging

PubMed Central

Kuzum, Duygu; Takano, Hajime; Shim, Euijae; Reed, Jason C; Juul, Halvor; Richardson, Andrew G.; de Vries, Julius; Bink, Hank; Dichter, Marc A.; Lucas, Timothy H.; Coulter, Douglas A.; Cubukcu, Ertugrul; Litt, Brian

2014-01-01

Calcium imaging is a versatile experimental approach capable of resolving single neurons with single-cell spatial resolution in the brain. Electrophysiological recordings provide high temporal, but limited spatial resolution, due to the geometrical inaccessibility of the brain. An approach that integrates the advantages of both techniques could provide new insights into functions of neural circuits. Here, we report a transparent, flexible neural electrode technology based on graphene, which enables simultaneous optical imaging and electrophysiological recording. We demonstrate that hippocampal slices can be imaged through transparent graphene electrodes by both confocal and two-photon microscopy without causing any light-induced artifacts in the electrical recordings. Graphene electrodes record high frequency bursting activity and slow synaptic potentials that are hard to resolve by multi-cellular calcium imaging. This transparent electrode technology may pave the way for high spatio-temporal resolution electrooptic mapping of the dynamic neuronal activity. PMID:25327632

Full-wave multiscale anisotropy tomography in Southern California

NASA Astrophysics Data System (ADS)

Lin, Yu-Pin; Zhao, Li; Hung, Shu-Huei

2014-12-01

Understanding the spatial variation of anisotropy in the upper mantle is important for characterizing the lithospheric deformation and mantle flow dynamics. In this study, we apply a full-wave approach to image the upper-mantle anisotropy in Southern California using 5954 SKS splitting data. Three-dimensional sensitivity kernels combined with a wavelet-based model parameterization are adopted in a multiscale inversion. Spatial resolution lengths are estimated based on a statistical resolution matrix approach, showing a finest resolution length of ~25 km in regions with densely distributed stations. The anisotropic model displays structural fabric in relation to surface geologic features such as the Salton Trough, the Transverse Ranges, and the San Andreas Fault. The depth variation of anisotropy does not suggest a lithosphere-asthenosphere decoupling. At long wavelengths, the fast directions of anisotropy are aligned with the absolute plate motion inside the Pacific and North American plates.

Rapid mapping of polarization switching through complete information acquisition

NASA Astrophysics Data System (ADS)

Somnath, Suhas; Belianinov, Alex; Kalinin, Sergei V.; Jesse, Stephen

2016-12-01

Polarization switching in ferroelectric and multiferroic materials underpins a broad range of current and emergent applications, ranging from random access memories to field-effect transistors, and tunnelling devices. Switching in these materials is exquisitely sensitive to local defects and microstructure on the nanometre scale, necessitating spatially resolved high-resolution studies of these phenomena. Classical piezoresponse force microscopy and spectroscopy, although providing necessary spatial resolution, are fundamentally limited in data acquisition rates and energy resolution. This limitation stems from their two-tiered measurement protocol that combines slow (~1 s) switching and fast (~10 kHz-1 MHz) detection waveforms. Here we develop an approach for rapid probing of ferroelectric switching using direct strain detection of material response to probe bias. This approach, facilitated by high-sensitivity electronics and adaptive filtering, enables spectroscopic imaging at a rate 3,504 times faster the current state of the art, achieving high-veracity imaging of polarization dynamics in complex microstructures.

Constraining regional scale carbon budgets at the US West Coast using a high-resolution atmospheric inverse modeling approach

NASA Astrophysics Data System (ADS)

Goeckede, M.; Michalak, A. M.; Vickers, D.; Turner, D.; Law, B.

2009-04-01

The study presented is embedded within the NACP (North American Carbon Program) West Coast project ORCA2, which aims at determining the regional carbon balance of the US states Oregon, California and Washington. Our work specifically focuses on the effect of disturbance history and climate variability, aiming at improving our understanding of e.g. drought stress and stand age on carbon sources and sinks in complex terrain with fine-scale variability in land cover types. The ORCA2 atmospheric inverse modeling approach has been set up to capture flux variability on the regional scale at high temporal and spatial resolution. Atmospheric transport is simulated coupling the mesoscale model WRF (Weather Research and Forecast) with the STILT (Stochastic Time Inverted Lagrangian Transport) footprint model. This setup allows identifying sources and sinks that influence atmospheric observations with highly resolved mass transport fields and realistic turbulent mixing. Terrestrial biosphere carbon fluxes are simulated at spatial resolutions of up to 1km and subdaily timesteps, considering effects of ecoregion, land cover type and disturbance regime on the carbon budgets. Our approach assimilates high-precision atmospheric CO2 concentration measurements and eddy-covariance data from several sites throughout the model domain, as well as high-resolution remote sensing products (e.g. LandSat, MODIS) and interpolated surface meteorology (DayMet, SOGS, PRISM). We present top-down modeling results that have been optimized using Bayesian inversion, reflecting the information on regional scale carbon processes provided by the network of high-precision CO2 observations. We address the level of detail (e.g. spatial and temporal resolution) that can be resolved by top-down modeling on the regional scale, given the uncertainties introduced by various sources for model-data mismatch. Our results demonstrate the importance of accurate modeling of carbon-water coupling, with the representation of water availability and drought stress playing a dominant role to capture spatially variable CO2 exchange rates in a region characterized by strong climatic gradients.

Assessing population exposure for landslide risk analysis using dasymetric cartography

NASA Astrophysics Data System (ADS)

Garcia, Ricardo A. C.; Oliveira, Sérgio C.; Zêzere, José L.

2016-12-01

Assessing the number and locations of exposed people is a crucial step in landslide risk management and emergency planning. The available population statistical data frequently have insufficient detail for an accurate assessment of potentially exposed people to hazardous events, mainly when they occur at the local scale, such as with landslides. The present study aims to apply dasymetric cartography to improving population spatial resolution and to assess the potentially exposed population. An additional objective is to compare the results with those obtained with a more common approach that uses, as spatial units, basic census units, which are the best spatial data disaggregation and detailed information available for regional studies in Portugal. Considering the Portuguese census data and a layer of residential building footprint, which was used as ancillary information, the number of exposed inhabitants differs significantly according to the approach used. When the census unit approach is used, considering the three highest landslide susceptible classes, the number of exposed inhabitants is in general overestimated. Despite the associated uncertainties of a general cost-benefit analysis, the presented methodology seems to be a reliable approach for gaining a first approximation of a more detailed estimation of exposed people. The approach based on dasymetric cartography allows the spatial resolution of population over large areas to be increased and enables the use of detailed landslide susceptibility maps, which are valuable for improving the exposed population assessment.

Extrapolating active layer thickness measurements across Arctic polygonal terrain using LiDAR and NDVI data sets.

PubMed

Gangodagamage, Chandana; Rowland, Joel C; Hubbard, Susan S; Brumby, Steven P; Liljedahl, Anna K; Wainwright, Haruko; Wilson, Cathy J; Altmann, Garrett L; Dafflon, Baptiste; Peterson, John; Ulrich, Craig; Tweedie, Craig E; Wullschleger, Stan D

2014-08-01

Landscape attributes that vary with microtopography, such as active layer thickness ( ALT ), are labor intensive and difficult to document effectively through in situ methods at kilometer spatial extents, thus rendering remotely sensed methods desirable. Spatially explicit estimates of ALT can provide critically needed data for parameterization, initialization, and evaluation of Arctic terrestrial models. In this work, we demonstrate a new approach using high-resolution remotely sensed data for estimating centimeter-scale ALT in a 5 km 2 area of ice-wedge polygon terrain in Barrow, Alaska. We use a simple regression-based, machine learning data-fusion algorithm that uses topographic and spectral metrics derived from multisensor data (LiDAR and WorldView-2) to estimate ALT (2 m spatial resolution) across the study area. Comparison of the ALT estimates with ground-based measurements, indicates the accuracy (r 2  = 0.76, RMSE ±4.4 cm) of the approach. While it is generally accepted that broad climatic variability associated with increasing air temperature will govern the regional averages of ALT , consistent with prior studies, our findings using high-resolution LiDAR and WorldView-2 data, show that smaller-scale variability in ALT is controlled by local eco-hydro-geomorphic factors. This work demonstrates a path forward for mapping ALT at high spatial resolution and across sufficiently large regions for improved understanding and predictions of coupled dynamics among permafrost, hydrology, and land-surface processes from readily available remote sensing data.

High-resolution wavefront control of high-power laser systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brase, J; Brown, C; Carrano, C

1999-07-08

Nearly every new large-scale laser system application at LLNL has requirements for beam control which exceed the current level of available technology. For applications such as inertial confinement fusion, laser isotope separation, laser machining, and laser the ability to transport significant power to a target while maintaining good beam quality is critical. There are many ways that laser wavefront quality can be degraded. Thermal effects due to the interaction of high-power laser or pump light with the internal optical components or with the ambient gas are common causes of wavefront degradation. For many years, adaptive optics based on thing deformablemore » glass mirrors with piezoelectric or electrostrictive actuators have be used to remove the low-order wavefront errors from high-power laser systems. These adaptive optics systems have successfully improved laser beam quality, but have also generally revealed additional high-spatial-frequency errors, both because the low-order errors have been reduced and because deformable mirrors have often introduced some high-spatial-frequency components due to manufacturing errors. Many current and emerging laser applications fall into the high-resolution category where there is an increased need for the correction of high spatial frequency aberrations which requires correctors with thousands of degrees of freedom. The largest Deformable Mirrors currently available have less than one thousand degrees of freedom at a cost of approximately $1M. A deformable mirror capable of meeting these high spatial resolution requirements would be cost prohibitive. Therefore a new approach using a different wavefront control technology is needed. One new wavefront control approach is the use of liquid-crystal (LC) spatial light modulator (SLM) technology for the controlling the phase of linearly polarized light. Current LC SLM technology provides high-spatial-resolution wavefront control, with hundreds of thousands of degrees of freedom, more than two orders of magnitude greater than the best Deformable Mirrors currently made. Even with the increased spatial resolution, the cost of these devices is nearly two orders of magnitude less than the cost of the largest deformable mirror.« less

Optimization as a Tool for Consistency Maintenance in Multi-Resolution Simulation

NASA Technical Reports Server (NTRS)

Drewry, Darren T; Reynolds, Jr , Paul F; Emanuel, William R

2006-01-01

The need for new approaches to the consistent simulation of related phenomena at multiple levels of resolution is great. While many fields of application would benefit from a complete and approachable solution to this problem, such solutions have proven extremely difficult. We present a multi-resolution simulation methodology that uses numerical optimization as a tool for maintaining external consistency between models of the same phenomena operating at different levels of temporal and/or spatial resolution. Our approach follows from previous work in the disparate fields of inverse modeling and spacetime constraint-based animation. As a case study, our methodology is applied to two environmental models of forest canopy processes that make overlapping predictions under unique sets of operating assumptions, and which execute at different temporal resolutions. Experimental results are presented and future directions are addressed.

Interlaced X-ray diffraction computed tomography

PubMed Central

Vamvakeros, Antonios; Jacques, Simon D. M.; Di Michiel, Marco; Senecal, Pierre; Middelkoop, Vesna; Cernik, Robert J.; Beale, Andrew M.

2016-01-01

An X-ray diffraction computed tomography data-collection strategy that allows, post experiment, a choice between temporal and spatial resolution is reported. This strategy enables time-resolved studies on comparatively short timescales, or alternatively allows for improved spatial resolution if the system under study, or components within it, appear to be unchanging. The application of the method for studying an Mn–Na–W/SiO2 fixed-bed reactor in situ is demonstrated. Additionally, the opportunities to improve the data-collection strategy further, enabling post-collection tuning between statistical, temporal and spatial resolutions, are discussed. In principle, the interlaced scanning approach can also be applied to other pencil-beam tomographic techniques, like X-ray fluorescence computed tomography, X-ray absorption fine structure computed tomography, pair distribution function computed tomography and tomographic scanning transmission X-ray microscopy. PMID:27047305

Exploiting MISR products at the full spatial resolution (275m) to document changes in land properties in and around the Kruger National Park, South Africa

NASA Astrophysics Data System (ADS)

Verstraete, M. M.; Hunt, L. A.; Pinty, B.; Clerici, M.; Scholes, R. J.

2009-12-01

The MISR instrument on NASA's Terra platform has been acquiring data globally and continuously for almost 10 years. A wide range of atmospheric and land products are operationally generated at the LaRC ASDC, at spatial resolutions of 1.1 km or coarser. Yet, the intrinsic spatial resolution of that sensor is 275m and 12 out of the 36 spectro-directional data channels are transmitted to the ground segment at that resolution. Recent algorithmic developments have permitted us to reconstruct reasonable estimates of the other 24 channels and to account for atmospheric effects at the full original spatial resolution. Spectro-directional reflectances have been processed to characterize the anisotropy of observed land surfaces and then optimally estimate various geophysical properties of the environment such as the fluxes of radiation in and out of plant canopies, the albedo, FAPAR, etc. These detailed products allow us to investigate ecological and environmental changes in much greater spatial and thematic detail than was previously possible. The paper outlines the various methodological steps implemented and exhibits concrete results for a region of moderate size (280 by 380 km) in South Africa. Practical downstream applications of this approach include monitoring desertification and biomass burning, documenting urbanization or characterizing the phenology of vegetation.

Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery

NASA Astrophysics Data System (ADS)

Qin, Yuanwei; Xiao, Xiangming; Dong, Jinwei; Zhou, Yuting; Zhu, Zhe; Zhang, Geli; Du, Guoming; Jin, Cui; Kou, Weili; Wang, Jie; Li, Xiangping

2015-07-01

Accurate and timely rice paddy field maps with a fine spatial resolution would greatly improve our understanding of the effects of paddy rice agriculture on greenhouse gases emissions, food and water security, and human health. Rice paddy field maps were developed using optical images with high temporal resolution and coarse spatial resolution (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)) or low temporal resolution and high spatial resolution (e.g., Landsat TM/ETM+). In the past, the accuracy and efficiency for rice paddy field mapping at fine spatial resolutions were limited by the poor data availability and image-based algorithms. In this paper, time series MODIS and Landsat ETM+/OLI images, and the pixel- and phenology-based algorithm are used to map paddy rice planting area. The unique physical features of rice paddy fields during the flooding/open-canopy period are captured with the dynamics of vegetation indices, which are then used to identify rice paddy fields. The algorithm is tested in the Sanjiang Plain (path/row 114/27) in China in 2013. The overall accuracy of the resulted map of paddy rice planting area generated by both Landsat ETM+ and OLI is 97.3%, when evaluated with areas of interest (AOIs) derived from geo-referenced field photos. The paddy rice planting area map also agrees reasonably well with the official statistics at the level of state farms (R2 = 0.94). These results demonstrate that the combination of fine spatial resolution images and the phenology-based algorithm can provide a simple, robust, and automated approach to map the distribution of paddy rice agriculture in a year.

Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery.

PubMed

Qin, Yuanwei; Xiao, Xiangming; Dong, Jinwei; Zhou, Yuting; Zhu, Zhe; Zhang, Geli; Du, Guoming; Jin, Cui; Kou, Weili; Wang, Jie; Li, Xiangping

2015-07-01

Accurate and timely rice paddy field maps with a fine spatial resolution would greatly improve our understanding of the effects of paddy rice agriculture on greenhouse gases emissions, food and water security, and human health. Rice paddy field maps were developed using optical images with high temporal resolution and coarse spatial resolution (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)) or low temporal resolution and high spatial resolution (e.g., Landsat TM/ETM+). In the past, the accuracy and efficiency for rice paddy field mapping at fine spatial resolutions were limited by the poor data availability and image-based algorithms. In this paper, time series MODIS and Landsat ETM+/OLI images, and the pixel- and phenology-based algorithm are used to map paddy rice planting area. The unique physical features of rice paddy fields during the flooding/open-canopy period are captured with the dynamics of vegetation indices, which are then used to identify rice paddy fields. The algorithm is tested in the Sanjiang Plain (path/row 114/27) in China in 2013. The overall accuracy of the resulted map of paddy rice planting area generated by both Landsat ETM+ and OLI is 97.3%, when evaluated with areas of interest (AOIs) derived from geo-referenced field photos. The paddy rice planting area map also agrees reasonably well with the official statistics at the level of state farms ( R 2 = 0.94). These results demonstrate that the combination of fine spatial resolution images and the phenology-based algorithm can provide a simple, robust, and automated approach to map the distribution of paddy rice agriculture in a year.

Using High Spatial Resolution Digital Imagery

DTIC Science & Technology

2005-02-01

digital base maps were high resolution U.S. Geological Survey (USGS) Digital Orthophoto Quarter Quadrangles (DOQQ). The Root Mean Square Errors (RMSE...next step was to assign real world coordinates to the linear im- age. The mosaics were geometrically registered to the panchromatic orthophotos ...useable thematic map from high-resolution imagery. A more practical approach may be to divide the Refuge into a set of smaller areas, or tiles

A Spatial Framework to Map Heat Health Risks at Multiple Scales.

PubMed

Ho, Hung Chak; Knudby, Anders; Huang, Wei

2015-12-18

In the last few decades extreme heat events have led to substantial excess mortality, most dramatically in Central Europe in 2003, in Russia in 2010, and even in typically cool locations such as Vancouver, Canada, in 2009. Heat-related morbidity and mortality is expected to increase over the coming centuries as the result of climate-driven global increases in the severity and frequency of extreme heat events. Spatial information on heat exposure and population vulnerability may be combined to map the areas of highest risk and focus mitigation efforts there. However, a mismatch in spatial resolution between heat exposure and vulnerability data can cause spatial scale issues such as the Modifiable Areal Unit Problem (MAUP). We used a raster-based model to integrate heat exposure and vulnerability data in a multi-criteria decision analysis, and compared it to the traditional vector-based model. We then used the Getis-Ord G(i) index to generate spatially smoothed heat risk hotspot maps from fine to coarse spatial scales. The raster-based model allowed production of maps at spatial resolution, more description of local-scale heat risk variability, and identification of heat-risk areas not identified with the vector-based approach. Spatial smoothing with the Getis-Ord G(i) index produced heat risk hotspots from local to regional spatial scale. The approach is a framework for reducing spatial scale issues in future heat risk mapping, and for identifying heat risk hotspots at spatial scales ranging from the block-level to the municipality level.

A space-time multiscale modelling of Earth's gravity field variations

NASA Astrophysics Data System (ADS)

Wang, Shuo; Panet, Isabelle; Ramillien, Guillaume; Guilloux, Frédéric

2017-04-01

The mass distribution within the Earth varies over a wide range of spatial and temporal scales, generating variations in the Earth's gravity field in space and time. These variations are monitored by satellites as the GRACE mission, with a 400 km spatial resolution and 10 days to 1 month temporal resolution. They are expressed in the form of gravity field models, often with a fixed spatial or temporal resolution. The analysis of these models allows us to study the mass transfers within the Earth system. Here, we have developed space-time multi-scale models of the gravity field, in order to optimize the estimation of gravity signals resulting from local processes at different spatial and temporal scales, and to adapt the time resolution of the model to its spatial resolution according to the satellites sampling. For that, we first build a 4D wavelet family combining spatial Poisson wavelets with temporal Haar wavelets. Then, we set-up a regularized inversion of inter-satellites gravity potential differences in a bayesian framework, to estimate the model parameters. To build the prior, we develop a spectral analysis, localized in time and space, of geophysical models of mass transport and associated gravity variations. Finally, we test our approach to the reconstruction of space-time variations of the gravity field due to hydrology. We first consider a global distribution of observations along the orbit, from a simplified synthetic hydrology signal comprising only annual variations at large spatial scales. Then, we consider a regional distribution of observations in Africa, and a larger number of spatial and temporal scales. We test the influence of an imperfect prior and discuss our results.

Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field.

PubMed

Nie, Jingxin; Xue, Zhong; Liu, Tianming; Young, Geoffrey S; Setayesh, Kian; Guo, Lei; Wong, Stephen T C

2009-09-01

A variety of algorithms have been proposed for brain tumor segmentation from multi-channel sequences, however, most of them require isotropic or pseudo-isotropic resolution of the MR images. Although co-registration and interpolation of low-resolution sequences, such as T2-weighted images, onto the space of the high-resolution image, such as T1-weighted image, can be performed prior to the segmentation, the results are usually limited by partial volume effects due to interpolation of low-resolution images. To improve the quality of tumor segmentation in clinical applications where low-resolution sequences are commonly used together with high-resolution images, we propose the algorithm based on Spatial accuracy-weighted Hidden Markov random field and Expectation maximization (SHE) approach for both automated tumor and enhanced-tumor segmentation. SHE incorporates the spatial interpolation accuracy of low-resolution images into the optimization procedure of the Hidden Markov Random Field (HMRF) to segment tumor using multi-channel MR images with different resolutions, e.g., high-resolution T1-weighted and low-resolution T2-weighted images. In experiments, we evaluated this algorithm using a set of simulated multi-channel brain MR images with known ground-truth tissue segmentation and also applied it to a dataset of MR images obtained during clinical trials of brain tumor chemotherapy. The results show that more accurate tumor segmentation results can be obtained by comparing with conventional multi-channel segmentation algorithms.

How Cities Breathe: Ground-Referenced, Airborne Hyperspectral Imaging Precursor Measurements To Space-Based Monitoring

NASA Technical Reports Server (NTRS)

Leifer, Ira; Tratt, David; Quattrochi, Dale; Bovensmann, Heinrich; Gerilowski, Konstantin; Buchwitz, Michael; Burrows, John

2013-01-01

Methane's (CH4) large global warming potential (Shindell et al., 2012) and likely increasing future emissions due to global warming feedbacks emphasize its importance to anthropogenic greenhouse warming (IPCC, 2007). Furthermore, CH4 regulation has far greater near-term climate change mitigation potential versus carbon dioxide CO2, the other major anthropogenic Greenhouse Gas (GHG) (Shindell et al., 2009). Uncertainties in CH4 budgets arise from the poor state of knowledge of CH4 sources - in part from a lack of sufficiently accurate assessments of the temporal and spatial emissions and controlling factors of highly variable anthropogenic and natural CH4 surface fluxes (IPCC, 2007) and the lack of global-scale (satellite) data at sufficiently high spatial resolution to resolve sources. Many important methane (and other trace gases) sources arise from urban and mega-urban landscapes where anthropogenic activities are centered - most of humanity lives in urban areas. Studying these complex landscape tapestries is challenged by a wide and varied range of activities at small spatial scale, and difficulty in obtaining up-to-date landuse data in the developed world - a key desire of policy makers towards development of effective regulations. In the developing world, challenges are multiplied with additional political access challenges. As high spatial resolution satellite and airborne data has become available, activity mapping applications have blossomed - i.e., Google maps; however, tap a minute fraction of remote sensing capabilities due to limited (three band) spectral information. Next generation approaches that incorporate high spatial resolution hyperspectral and ultraspectral data will allow detangling of the highly heterogeneous usage megacity patterns by providing diagnostic identification of chemical composition from solids (refs) to gases (refs). To properly enable these next generation technologies for megacity include atmospheric radiative transfer modeling the complex and often aerosol laden, humid, urban microclimates, atmospheric transport and profile monitoring, spatial resolution, temporal cycles (diurnal and seasonal which involve interactions with the surrounding environment diurnal and seasonal cycles) and representative measurement approaches given traffic realities. Promising approaches incorporate contemporaneous airborne remote sensing and in situ measurements, nocturnal surface surveys, with ground station measurement

Detecting Uniform Areas for Vicarious Calibration using Landsat TM Imagery: A Study using the Arabian and Saharan Deserts

NASA Technical Reports Server (NTRS)

Hilbert, Kent; Pagnutti, Mary; Ryan, Robert; Zanoni, Vicki

2002-01-01

This paper discusses a method for detecting spatially uniform sites need for radiometric characterization of remote sensing satellites. Such information is critical for scientific research applications of imagery having moderate to high resolutions (<30-m ground sampling distance (GSD)). Previously published literature indicated that areas with the African Saharan and Arabian deserts contained extremely uniform sites with respect to spatial characteristics. We developed an algorithm for detecting site uniformity and applied it to orthorectified Landsat Thematic Mapper (TM) imagery over eight uniform regions of interest. The algorithm's results were assessed using both medium-resolution (30-m GSD) Landsat 7 ETM+ and fine-resolution (<5-m GSD) IKONOS multispectral data collected over sites in Libya and Mali. Fine-resolution imagery over a Libyan site exhibited less than 1 percent nonuniformity. The research shows that Landsat TM products appear highly useful for detecting potential calibration sites for system characterization. In particular, the approach detected spatially uniform regions that frequently occur at multiple scales of observation.

The influence of spectral and spatial resolution in classification approaches: Landsat TM data vs. Hyperspectral data

NASA Astrophysics Data System (ADS)

Rodríguez-Galiano, Víctor; Garcia-Soldado, Maria José; Chica-Olmo, Mario

The importance of accurate and timely information describing the nature and extent of land and natural resources is increasing especially in rapidly growing metropolitan areas. While metropolitan area decision makers are in constant need of current geospatial information on patterns and trends in land cover and land use, relatively little researchers has investigated the influence of the satellite data resolution for monitoring geo-enviromental information. In this research a suite of remote sensing and GIS techniques is applied in a land use mapping study. The main task is to asses the influence of the spatial and spectral resolution in the separability between classes and in the classificatiońs accuracy. This study has been focused in a very dynamical area with respect to land use, located in the province of Granada (SE of Spain). The classifications results of the Airborne Hyperspectral Scanner (AHS, Daedalus Enterprise Inc., WA, EEUU) at different spatial resolutions: 2, 4 and 6 m and Landsat 5 TM data have been compared.

Source-space ICA for MEG source imaging.

PubMed

Jonmohamadi, Yaqub; Jones, Richard D

2016-02-01

One of the most widely used approaches in electroencephalography/magnetoencephalography (MEG) source imaging is application of an inverse technique (such as dipole modelling or sLORETA) on the component extracted by independent component analysis (ICA) (sensor-space ICA + inverse technique). The advantage of this approach over an inverse technique alone is that it can identify and localize multiple concurrent sources. Among inverse techniques, the minimum-variance beamformers offer a high spatial resolution. However, in order to have both high spatial resolution of beamformer and be able to take on multiple concurrent sources, sensor-space ICA + beamformer is not an ideal combination. We propose source-space ICA for MEG as a powerful alternative approach which can provide the high spatial resolution of the beamformer and handle multiple concurrent sources. The concept of source-space ICA for MEG is to apply the beamformer first and then singular value decomposition + ICA. In this paper we have compared source-space ICA with sensor-space ICA both in simulation and real MEG. The simulations included two challenging scenarios of correlated/concurrent cluster sources. Source-space ICA provided superior performance in spatial reconstruction of source maps, even though both techniques performed equally from a temporal perspective. Real MEG from two healthy subjects with visual stimuli were also used to compare performance of sensor-space ICA and source-space ICA. We have also proposed a new variant of minimum-variance beamformer called weight-normalized linearly-constrained minimum-variance with orthonormal lead-field. As sensor-space ICA-based source reconstruction is popular in EEG and MEG imaging, and given that source-space ICA has superior spatial performance, it is expected that source-space ICA will supersede its predecessor in many applications.

Super-Resolution of Plant Disease Images for the Acceleration of Image-based Phenotyping and Vigor Diagnosis in Agriculture.

PubMed

Yamamoto, Kyosuke; Togami, Takashi; Yamaguchi, Norio

2017-11-06

Unmanned aerial vehicles (UAVs or drones) are a very promising branch of technology, and they have been utilized in agriculture-in cooperation with image processing technologies-for phenotyping and vigor diagnosis. One of the problems in the utilization of UAVs for agricultural purposes is the limitation in flight time. It is necessary to fly at a high altitude to capture the maximum number of plants in the limited time available, but this reduces the spatial resolution of the captured images. In this study, we applied a super-resolution method to the low-resolution images of tomato diseases to recover detailed appearances, such as lesions on plant organs. We also conducted disease classification using high-resolution, low-resolution, and super-resolution images to evaluate the effectiveness of super-resolution methods in disease classification. Our results indicated that the super-resolution method outperformed conventional image scaling methods in spatial resolution enhancement of tomato disease images. The results of disease classification showed that the accuracy attained was also better by a large margin with super-resolution images than with low-resolution images. These results indicated that our approach not only recovered the information lost in low-resolution images, but also exerted a beneficial influence on further image analysis. The proposed approach will accelerate image-based phenotyping and vigor diagnosis in the field, because it not only saves time to capture images of a crop in a cultivation field but also secures the accuracy of these images for further analysis.

Super-Resolution of Plant Disease Images for the Acceleration of Image-based Phenotyping and Vigor Diagnosis in Agriculture

PubMed Central

Togami, Takashi; Yamaguchi, Norio

2017-01-01

Unmanned aerial vehicles (UAVs or drones) are a very promising branch of technology, and they have been utilized in agriculture—in cooperation with image processing technologies—for phenotyping and vigor diagnosis. One of the problems in the utilization of UAVs for agricultural purposes is the limitation in flight time. It is necessary to fly at a high altitude to capture the maximum number of plants in the limited time available, but this reduces the spatial resolution of the captured images. In this study, we applied a super-resolution method to the low-resolution images of tomato diseases to recover detailed appearances, such as lesions on plant organs. We also conducted disease classification using high-resolution, low-resolution, and super-resolution images to evaluate the effectiveness of super-resolution methods in disease classification. Our results indicated that the super-resolution method outperformed conventional image scaling methods in spatial resolution enhancement of tomato disease images. The results of disease classification showed that the accuracy attained was also better by a large margin with super-resolution images than with low-resolution images. These results indicated that our approach not only recovered the information lost in low-resolution images, but also exerted a beneficial influence on further image analysis. The proposed approach will accelerate image-based phenotyping and vigor diagnosis in the field, because it not only saves time to capture images of a crop in a cultivation field but also secures the accuracy of these images for further analysis. PMID:29113104

Distributed fiber optic vibration sensor with enhanced response bandwidth and high signal-to-noise ratio

NASA Astrophysics Data System (ADS)

Chen, Dian; Liu, Qingwen; Fan, Xinyu; He, Zuyuan

2017-04-01

A novel distributed fiber-optic vibration sensor (DVS) is proposed based on multi-pulse time-gated digital optical frequency domain reflectometry (TGD-OFDR), which can solve both the trade-off between the maximum measurable distance and the spatial resolution, and the one between the measurement distance and the vibration response bandwidth. A 21-kHz vibration is detected experimentally over 10-kilometer-long fiber, with a signal-to-noise ratio approaching 25 dB and a spatial resolution of 10 m.

Cloud field classification based upon high spatial resolution textural features. I - Gray level co-occurrence matrix approach

NASA Technical Reports Server (NTRS)

Welch, R. M.; Sengupta, S. K.; Chen, D. W.

1988-01-01

Stratocumulus, cumulus, and cirrus clouds were identified on the basis of cloud textural features which were derived from a single high-resolution Landsat MSS NIR channel using a stepwise linear discriminant analysis. It is shown that, using this method, it is possible to distinguish high cirrus clouds from low clouds with high accuracy on the basis of spatial brightness patterns. The largest probability of misclassification is associated with confusion between the stratocumulus breakup regions and the fair-weather cumulus.

Atomic-resolution single-spin magnetic resonance detection concept based on tunneling force microscopy

NASA Astrophysics Data System (ADS)

Payne, A.; Ambal, K.; Boehme, C.; Williams, C. C.

2015-05-01

A study of a force detected single-spin magnetic resonance measurement concept with atomic spatial resolution is presented. The method is based upon electrostatic force detection of spin-selection rule controlled single-electron tunneling between two electrically isolated paramagnetic states. Single-spin magnetic resonance detection is possible by measuring the force detected tunneling charge noise on and off spin resonance. Simulation results of this charge noise, based upon physical models of the tunneling and spin physics, are directly compared to measured atomic force microscopy system noise. The results show that the approach could provide single-spin measurement of electrically isolated qubit states with atomic spatial resolution at room temperature.

Comparison of Object-Based Image Analysis Approaches to Mapping New Buildings in Accra, Ghana Using Multi-Temporal QuickBird Satellite Imagery

PubMed Central

Tsai, Yu Hsin; Stow, Douglas; Weeks, John

2013-01-01

The goal of this study was to map and quantify the number of newly constructed buildings in Accra, Ghana between 2002 and 2010 based on high spatial resolution satellite image data. Two semi-automated feature detection approaches for detecting and mapping newly constructed buildings based on QuickBird very high spatial resolution satellite imagery were analyzed: (1) post-classification comparison; and (2) bi-temporal layerstack classification. Feature Analyst software based on a spatial contextual classifier and ENVI Feature Extraction that uses a true object-based image analysis approach of image segmentation and segment classification were evaluated. Final map products representing new building objects were compared and assessed for accuracy using two object-based accuracy measures, completeness and correctness. The bi-temporal layerstack method generated more accurate results compared to the post-classification comparison method due to less confusion with background objects. The spectral/spatial contextual approach (Feature Analyst) outperformed the true object-based feature delineation approach (ENVI Feature Extraction) due to its ability to more reliably delineate individual buildings of various sizes. Semi-automated, object-based detection followed by manual editing appears to be a reliable and efficient approach for detecting and enumerating new building objects. A bivariate regression analysis was performed using neighborhood-level estimates of new building density regressed on a census-derived measure of socio-economic status, yielding an inverse relationship with R2 = 0.31 (n = 27; p = 0.00). The primary utility of the new building delineation results is to support spatial analyses of land cover and land use and demographic change. PMID:24415810

Identification of mosquito larval habitats in high resolution satellite data

NASA Astrophysics Data System (ADS)

Kiang, Richard K.; Hulina, Stephanie M.; Masuoka, Penny M.; Claborn, David M.

2003-09-01

Mosquito-born infectious diseases are a serious public health concern, not only for the less developed countries, but also for developed countries like the U.S. Larviciding is an effective method for vector control and adverse effects to non-target species are minimized when mosquito larval habitats are properly surveyed and treated. Remote sensing has proven to be a useful technique for large-area ground cover mapping, and hence, is an ideal tool for identifying potential larval habitats. Locating small larval habitats, however, requires data with very high spatial resolution. Textural and contextual characteristics become increasingly evident at higher spatial resolution. Per-pixel classification often leads to suboptimal results. In this study, we use pan-sharpened Ikonos data, with a spatial resolution approaching 1 meter, to classify potential mosquito larval habitats for a test site in South Korea. The test site is in a predominantly agricultural region. When spatial characteristics were used in conjunction with spectral data, reasonably good classification accuracy was obtained for the test site. In particular, irrigation and drainage ditches are important larval habitats but their footprints are too small to be detected with the original spectral data at 4-meter resolution. We show that the ditches are detectable using automated classification on pan-sharpened data.

Utilising Structure-From-Motion Approaches to Develop a Spatial Understanding of Soil Erosion Processes, in an Experimental Setting.

NASA Astrophysics Data System (ADS)

Benaud, P.; Anderson, K.; Quine, T. A.; James, M. R.; Quinton, J.; Brazier, R. E.

2016-12-01

While total sediment capture can accurately quantify soil loss via water erosion, it isn't practical at the field scale and provides little information on the spatial nature of soil erosion processes. Consequently, high-resolution, remote sensing, point cloud data provide an alternative method for quantifying soil loss. The accessibility of Structure-from-Motion Multi-Stereo View (SfM) and the potential for multi-temporal applications, offers an exciting opportunity to spatially quantify soil erosion. Accordingly, published research provides examples of the successful quantification of large erosion features and events, to centimetre accuracy. Through rigorous control of the camera and image network geometry, the centimetre accuracy achievable at the field scale, can translate to sub-millimetre accuracies within a laboratory environment. Accordingly, this study looks to understand how the ultra-high-resolution spatial information on soil surface topography, derived from SfM, can be integrated with a multi-element sediment tracer to develop a mechanistic understanding of rill and inter-rill erosion, under experimental conditions. A rainfall simulator was used to create three soil surface conditions; compaction and rainsplash, inter-rill erosion, and rill erosion, at two experimental scales (0.15 m2 and 3 m2). Total sediment capture was the primary validation for the experiments, allowing the comparison between structurally and volumetrically derived change, and true soil loss. A Terrestrial Laser Scanner (resolution of ca. 0.8mm) has been employed to assess spatial discrepancies within the SfM data sets and to provide an alternative measure of volumetric change. Preliminary results show the SfM approach used can achieve a ground resolution of less than 0.2 mm per pixel, and a RMSE of less than 0.3 mm. Consequently, it is expected that the ultra-high-resolution SfM point clouds can be utilised to provide a detailed assessment of soil loss via water erosion processes.

Image reconstruction and system modeling techniques for virtual-pinhole PET insert systems

PubMed Central

Keesing, Daniel B; Mathews, Aswin; Komarov, Sergey; Wu, Heyu; Song, Tae Yong; O'Sullivan, Joseph A; Tai, Yuan-Chuan

2012-01-01

Virtual-pinhole PET (VP-PET) imaging is a new technology in which one or more high-resolution detector modules are integrated into a conventional PET scanner with lower-resolution detectors. It can locally enhance the spatial resolution and contrast recovery near the add-on detectors, and depending on the configuration, may also increase the sensitivity of the system. This novel scanner geometry makes the reconstruction problem more challenging compared to the reconstruction of data from a standalone PET scanner, as new techniques are needed to model and account for the non-standard acquisition. In this paper, we present a general framework for fully 3D modeling of an arbitrary VP-PET insert system. The model components are incorporated into a statistical reconstruction algorithm to estimate an image from the multi-resolution data. For validation, we apply the proposed model and reconstruction approach to one of our custom-built VP-PET systems – a half-ring insert device integrated into a clinical PET/CT scanner. Details regarding the most important implementation issues are provided. We show that the proposed data model is consistent with the measured data, and that our approach can lead to reconstructions with improved spatial resolution and lesion detectability. PMID:22490983

Biomechanics meets the ecological niche: the importance of temporal data resolution.

PubMed

Kearney, Michael R; Matzelle, Allison; Helmuth, Brian

2012-03-15

The emerging field of mechanistic niche modelling aims to link the functional traits of organisms to their environments to predict survival, reproduction, distribution and abundance. This approach has great potential to increase our understanding of the impacts of environmental change on individuals, populations and communities by providing functional connections between physiological and ecological response to increasingly available spatial environmental data. By their nature, such mechanistic models are more data intensive in comparison with the more widely applied correlative approaches but can potentially provide more spatially and temporally explicit predictions, which are often needed by decision makers. A poorly explored issue in this context is the appropriate level of temporal resolution of input data required for these models, and specifically the error in predictions that can be incurred through the use of temporally averaged data. Here, we review how biomechanical principles from heat-transfer and metabolic theory are currently being used as foundations for mechanistic niche models and consider the consequences of different temporal resolutions of environmental data for modelling the niche of a behaviourally thermoregulating terrestrial lizard. We show that fine-scale temporal resolution (daily) data can be crucial for unbiased inference of climatic impacts on survival, growth and reproduction. This is especially so for species with little capacity for behavioural buffering, because of behavioural or habitat constraints, and for detecting temporal trends. However, coarser-resolution data (long-term monthly averages) can be appropriate for mechanistic studies of climatic constraints on distribution and abundance limits in thermoregulating species at broad spatial scales.

Spatial downscaling of soil prediction models based on weighted generalized additive models in smallholder farm settings.

PubMed

Xu, Yiming; Smith, Scot E; Grunwald, Sabine; Abd-Elrahman, Amr; Wani, Suhas P; Nair, Vimala D

2017-09-11

Digital soil mapping (DSM) is gaining momentum as a technique to help smallholder farmers secure soil security and food security in developing regions. However, communications of the digital soil mapping information between diverse audiences become problematic due to the inconsistent scale of DSM information. Spatial downscaling can make use of accessible soil information at relatively coarse spatial resolution to provide valuable soil information at relatively fine spatial resolution. The objective of this research was to disaggregate the coarse spatial resolution soil exchangeable potassium (K ex ) and soil total nitrogen (TN) base map into fine spatial resolution soil downscaled map using weighted generalized additive models (GAMs) in two smallholder villages in South India. By incorporating fine spatial resolution spectral indices in the downscaling process, the soil downscaled maps not only conserve the spatial information of coarse spatial resolution soil maps but also depict the spatial details of soil properties at fine spatial resolution. The results of this study demonstrated difference between the fine spatial resolution downscaled maps and fine spatial resolution base maps is smaller than the difference between coarse spatial resolution base maps and fine spatial resolution base maps. The appropriate and economical strategy to promote the DSM technique in smallholder farms is to develop the relatively coarse spatial resolution soil prediction maps or utilize available coarse spatial resolution soil maps at the regional scale and to disaggregate these maps to the fine spatial resolution downscaled soil maps at farm scale.

Superresolution parallel magnetic resonance imaging: Application to functional and spectroscopic imaging

PubMed Central

Otazo, Ricardo; Lin, Fa-Hsuan; Wiggins, Graham; Jordan, Ramiro; Sodickson, Daniel; Posse, Stefan

2009-01-01

Standard parallel magnetic resonance imaging (MRI) techniques suffer from residual aliasing artifacts when the coil sensitivities vary within the image voxel. In this work, a parallel MRI approach known as Superresolution SENSE (SURE-SENSE) is presented in which acceleration is performed by acquiring only the central region of k-space instead of increasing the sampling distance over the complete k-space matrix and reconstruction is explicitly based on intra-voxel coil sensitivity variation. In SURE-SENSE, parallel MRI reconstruction is formulated as a superresolution imaging problem where a collection of low resolution images acquired with multiple receiver coils are combined into a single image with higher spatial resolution using coil sensitivities acquired with high spatial resolution. The effective acceleration of conventional gradient encoding is given by the gain in spatial resolution, which is dictated by the degree of variation of the different coil sensitivity profiles within the low resolution image voxel. Since SURE-SENSE is an ill-posed inverse problem, Tikhonov regularization is employed to control noise amplification. Unlike standard SENSE, for which acceleration is constrained to the phase-encoding dimension/s, SURE-SENSE allows acceleration along all encoding directions — for example, two-dimensional acceleration of a 2D echo-planar acquisition. SURE-SENSE is particularly suitable for low spatial resolution imaging modalities such as spectroscopic imaging and functional imaging with high temporal resolution. Application to echo-planar functional and spectroscopic imaging in human brain is presented using two-dimensional acceleration with a 32-channel receiver coil. PMID:19341804

Application of Unmanned Aerial Systems in Spatial Downscaling of Landsat VIR imageries of Agricultural Fields

NASA Astrophysics Data System (ADS)

Torres, A.; Hassan Esfahani, L.; Ebtehaj, A.; McKee, M.

2016-12-01

While coarse space-time resolution of satellite observations in visible to near infrared (VIR) is a serious limiting factor for applications in precision agriculture, high resolution remotes sensing observation by the Unmanned Aerial Systems (UAS) systems are also site-specific and still practically restrictive for widespread applications in precision agriculture. We present a modern spatial downscaling approach that relies on new sparse approximation techniques. The downscaling approach learns from a large set of coincident low- and high-resolution satellite and UAS observations to effectively downscale the satellite imageries in VIR bands. We focus on field experiments using the AggieAirTM platform and Landsat 7 ETM+ and Landsat 8 OLI observations obtained in an intensive field campaign in 2013 over an agriculture field in Scipio, Utah. The results show that the downscaling methods can effectively increase the resolution of Landsat VIR imageries by the order of 2 to 4 from 30 m to 15 and 7.5 m, respectively. Specifically, on average, the downscaling method reduces the root mean squared errors up to 26%, considering bias corrected AggieAir imageries as the reference.

Geometrical superresolved imaging using nonperiodic spatial masking.

PubMed

Borkowski, Amikam; Zalevsky, Zeev; Javidi, Bahram

2009-03-01

The resolution of every imaging system is limited either by the F-number of its optics or by the geometry of its detection array. The geometrical limitation is caused by lack of spatial sampling points as well as by the shape of every sampling pixel that generates spectral low-pass filtering. We present a novel approach to overcome the low-pass filtering that is due to the shape of the sampling pixels. The approach combines special algorithms together with spatial masking placed in the intermediate image plane and eventually allows geometrical superresolved imaging without relation to the actual shape of the pixels.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smith, Donald F.; Schulz, Carl; Konijnenburg, Marco

High-resolution Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry imaging enables the spatial mapping and identification of biomolecules from complex surfaces. The need for long time-domain transients, and thus large raw file sizes, results in a large amount of raw data (“big data”) that must be processed efficiently and rapidly. This can be compounded by largearea imaging and/or high spatial resolution imaging. For FT-ICR, data processing and data reduction must not compromise the high mass resolution afforded by the mass spectrometer. The continuous mode “Mosaic Datacube” approach allows high mass resolution visualization (0.001 Da) of mass spectrometry imaging data, butmore » requires additional processing as compared to featurebased processing. We describe the use of distributed computing for processing of FT-ICR MS imaging datasets with generation of continuous mode Mosaic Datacubes for high mass resolution visualization. An eight-fold improvement in processing time is demonstrated using a Dutch nationally available cloud service.« less

Fusion of spectral and panchromatic images using false color mapping and wavelet integrated approach

NASA Astrophysics Data System (ADS)

Zhao, Yongqiang; Pan, Quan; Zhang, Hongcai

2006-01-01

With the development of sensory technology, new image sensors have been introduced that provide a greater range of information to users. But as the power limitation of radiation, there will always be some trade-off between spatial and spectral resolution in the image captured by specific sensors. Images with high spatial resolution can locate objects with high accuracy, whereas images with high spectral resolution can be used to identify the materials. Many applications in remote sensing require fusing low-resolution imaging spectral images with panchromatic images to identify materials at high resolution in clutter. A pixel-based false color mapping and wavelet transform integrated fusion algorithm is presented in this paper, the resulting images have a higher information content than each of the original images and retain sensor-specific image information. The simulation results show that this algorithm can enhance the visibility of certain details and preserve the difference of different materials.

Spatial Resolution Versus Data Acquisition Efficiency in Mapping an Inhomogeneous System with Species Diffusion

DOE PAGES

Chen, Fengxiang; Zhang, Yong; Gfroerer, T. H.; ...

2015-06-02

Traditionally, spatially-resolved photoluminescence (PL) has been performed using a point-by-point scan mode with both excitation and detection occurring at the same spatial location. But with the availability of high quality detector arrays like CCDs, an imaging mode has become popular for performing spatially-resolved PL. By illuminating the entire area of interest and collecting the data simultaneously from all spatial locations, the measurement efficiency can be greatly improved. However, this new approach has proceeded under the implicit assumption of comparable spatial resolution. We show here that when carrier diffusion is present, the spatial resolution can actually differ substantially between the twomore » modes, with the less efficient scan mode being far superior. We apply both techniques in investigation of defects in a GaAs epilayer – where isolated singlet and doublet dislocations can be identified. A superposition principle is developed for solving the diffusion equation to extract the intrinsic carrier diffusion length, which can be applied to a system with arbitrarily distributed defects. The understanding derived from this work is significant for a broad range of problems in physics and beyond (for instance biology) – whenever the dynamics of generation, diffusion, and annihilation of species can be probed with either measurement mode.« less

Quantifying hyporheic exchange at high spatial resolution using natural temperature variations along a first-order stream

NASA Astrophysics Data System (ADS)

Westhoff, M. C.; Gooseff, M. N.; Bogaard, T. A.; Savenije, H. H. G.

2011-10-01

Hyporheic exchange is an important process that underpins stream ecosystem function, and there have been numerous ways to characterize and quantify exchange flow rates and hyporheic zone size. The most common approach, using conservative stream tracer experiments and 1-D solute transport modeling, results in oversimplified representations of the system. Here we present a new approach to quantify hyporheic exchange and the size of the hyporheic zone (HZ) using high-resolution temperature measurements and a coupled 1-D transient storage and energy balance model to simulate in-stream water temperatures. Distributed temperature sensing was used to observe in-stream water temperatures with a spatial and temporal resolution of 2 and 3 min, respectively. The hyporheic exchange coefficient (which describes the rate of exchange) and the volume of the HZ were determined to range between 0 and 2.7 × 10-3 s-1 and 0 and 0.032 m3 m-1, respectively, at a spatial resolution of 1-10 m, by simulating a time series of in-stream water temperatures along a 565 m long stretch of a small first-order stream in central Luxembourg. As opposed to conventional stream tracer tests, two advantages of this approach are that exchange parameters can be determined for any stream segment over which data have been collected and that the depth of the HZ can be estimated as well. Although the presented method was tested on a small stream, it has potential for any stream where rapid (in regard to time) temperature change of a few degrees can be obtained.

Phenomapping of rangelands in South Africa using time series of RapidEye data

NASA Astrophysics Data System (ADS)

Parplies, André; Dubovyk, Olena; Tewes, Andreas; Mund, Jan-Peter; Schellberg, Jürgen

2016-12-01

Phenomapping is an approach which allows the derivation of spatial patterns of vegetation phenology and rangeland productivity based on time series of vegetation indices. In our study, we propose a new spatial mapping approach which combines phenometrics derived from high resolution (HR) satellite time series with spatial logistic regression modeling to discriminate land management systems in rangelands. From the RapidEye time series for selected rangelands in South Africa, we calculated bi-weekly noise reduced Normalized Difference Vegetation Index (NDVI) images. For the growing season of 2011⿿2012, we further derived principal phenology metrics such as start, end and length of growing season and related phenological variables such as amplitude, left derivative and small integral of the NDVI curve. We then mapped these phenometrics across two different tenure systems, communal and commercial, at the very detailed spatial resolution of 5 m. The result of a binary logistic regression (BLR) has shown that the amplitude and the left derivative of the NDVI curve were statistically significant. These indicators are useful to discriminate commercial from communal rangeland systems. We conclude that phenomapping combined with spatial modeling is a powerful tool that allows efficient aggregation of phenology and productivity metrics for spatially explicit analysis of the relationships of crop phenology with site conditions and management. This approach has particular potential for disaggregated and patchy environments such as in farming systems in semi-arid South Africa, where phenology varies considerably among and within years. Further, we see a strong perspective for phenomapping to support spatially explicit modelling of vegetation.

Optimal attributes for the object based detection of giant reed in riparian habitats: A comparative study between Airborne High Spatial Resolution and WorldView-2 imagery

NASA Astrophysics Data System (ADS)

Fernandes, Maria Rosário; Aguiar, Francisca C.; Silva, João M. N.; Ferreira, Maria Teresa; Pereira, José M. C.

2014-10-01

Giant reed is an aggressive invasive plant of riparian ecosystems in many sub-tropical and warm-temperate regions, including Mediterranean Europe. In this study we tested a set of geometric, spectral and textural attributes in an object based image analysis (OBIA) approach to map giant reed invasions in riparian habitats. Bagging Classification and Regression Tree were used to select the optimal attributes and to build the classification rules sets. Mapping accuracy was performed using landscape metrics and the Kappa coefficient to compare the topographical and geometric similarity between the giant reed patches obtained with the OBIA map and with a validation map derived from on-screen digitizing. The methodology was applied in two high spatial resolution images: an airborne multispectral imagery and the newly WorldView-2 imagery. A temporal coverage of the airborne multispectral images was radiometrically calibrated with the IR-Mad transformation and used to assess the influence of the phenological variability of the invader. We found that optimal attributes for giant reed OBIA detection are a combination of spectral, geometric and textural information, with different scoring selection depending on the spectral and spatial characteristics of the imagery. WorldView-2 showed higher mapping accuracy (Kappa coefficient of 77%) and spectral attributes, including the newly yellow band, were preferentially selected, although a tendency to overestimate the total invaded area, due to the low spatial resolution (2 m of pixel size vs. 50 cm) was observed. When airborne images were used, geometric attributes were primarily selected and a higher spatial detail of the invasive patches was obtained, due to the higher spatial resolution. However, in highly heterogeneous landscapes, the low spectral resolution of the airborne images (4 bands instead of the 8 of WorldView-2) reduces the capability to detect giant reed patches. Giant reed displays peculiar spectral and geometric traits, at leaf, canopy and stand level, which makes the OBIA approach a very suitable technique for management purposes.

Examining fire-induced forest changes using novel remote sensing technique: a case study in a mixed pine-oak forest

NASA Astrophysics Data System (ADS)

Meng, R.; Wu, J.; Zhao, F. R.; Cook, B.; Hanavan, R. P.; Serbin, S.

2017-12-01

Fire-induced forest changes has long been a central focus for forest ecology and global carbon cycling studies, and is becoming a pressing issue for global change biologists particularly with the projected increases in the frequency and intensity of fire with a warmer and drier climate. Compared with time-consuming and labor intensive field-based approaches, remote sensing offers a promising way to efficiently assess fire effects and monitor post-fire forest responses across a range of spatial and temporal scales. However, traditional remote sensing studies relying on simple optical spectral indices or coarse resolution imagery still face a number of technical challenges, including confusion or contamination of the signal by understory dynamics and mixed pixels with moderate to coarse resolution data (>= 30 m). As such, traditional remote sensing may not meet the increasing demand for more ecologically-meaningful monitoring and quantitation of fire-induced forest changes. Here we examined the use of novel remote sensing technique (i.e. airborne imaging spectroscopy and LiDAR measurement, very high spatial resolution (VHR) space-borne multi-spectral measurement, and high temporal-spatial resolution UAS-based (Unmanned Aerial System) imagery), in combination with field and phenocam measurements to map forest burn severity across spatial scales, quantify crown-scale post-fire forest recovery rate, and track fire-induced phenology changes in the burned areas. We focused on a mixed pine-oak forest undergoing multiple fire disturbances for the past several years in Long Island, NY as a case study. We demonstrate that (1) forest burn severity mapping from VHR remote sensing measurement can capture crown-scale heterogeneous fire patterns over large-scale; (2) the combination of VHR optical and structural measurements provides an efficient means to remotely sense species-level post-fire forest responses; (3) the UAS-based remote sensing enables monitoring of fire-induced forest phenology changes at unprecedented temporal and spatial resolutions. This work provides the methodological approach monitor fire-induced forest changes in a spatially explicit manner across scales, with important implications for fire-related forest management and for constraining/benchmarking process models.

The 2015 super-resolution microscopy roadmap

NASA Astrophysics Data System (ADS)

Hell, Stefan W.; Sahl, Steffen J.; Bates, Mark; Zhuang, Xiaowei; Heintzmann, Rainer; Booth, Martin J.; Bewersdorf, Joerg; Shtengel, Gleb; Hess, Harald; Tinnefeld, Philip; Honigmann, Alf; Jakobs, Stefan; Testa, Ilaria; Cognet, Laurent; Lounis, Brahim; Ewers, Helge; Davis, Simon J.; Eggeling, Christian; Klenerman, David; Willig, Katrin I.; Vicidomini, Giuseppe; Castello, Marco; Diaspro, Alberto; Cordes, Thorben

2015-11-01

Far-field optical microscopy using focused light is an important tool in a number of scientific disciplines including chemical, (bio)physical and biomedical research, particularly with respect to the study of living cells and organisms. Unfortunately, the applicability of the optical microscope is limited, since the diffraction of light imposes limitations on the spatial resolution of the image. Consequently the details of, for example, cellular protein distributions, can be visualized only to a certain extent. Fortunately, recent years have witnessed the development of ‘super-resolution’ far-field optical microscopy (nanoscopy) techniques such as stimulated emission depletion (STED), ground state depletion (GSD), reversible saturated optical (fluorescence) transitions (RESOLFT), photoactivation localization microscopy (PALM), stochastic optical reconstruction microscopy (STORM), structured illumination microscopy (SIM) or saturated structured illumination microscopy (SSIM), all in one way or another addressing the problem of the limited spatial resolution of far-field optical microscopy. While SIM achieves a two-fold improvement in spatial resolution compared to conventional optical microscopy, STED, RESOLFT, PALM/STORM, or SSIM have all gone beyond, pushing the limits of optical image resolution to the nanometer scale. Consequently, all super-resolution techniques open new avenues of biomedical research. Because the field is so young, the potential capabilities of different super-resolution microscopy approaches have yet to be fully explored, and uncertainties remain when considering the best choice of methodology. Thus, even for experts, the road to the future is sometimes shrouded in mist. The super-resolution optical microscopy roadmap of Journal of Physics D: Applied Physics addresses this need for clarity. It provides guidance to the outstanding questions through a collection of short review articles from experts in the field, giving a thorough discussion on the concepts underlying super-resolution optical microscopy, the potential of different approaches, the importance of label optimization (such as reversible photoswitchable proteins) and applications in which these methods will have a significant impact. Mark Bates, Christian Eggeling

Satellite image time series simulation for environmental monitoring

NASA Astrophysics Data System (ADS)

Guo, Tao

2014-11-01

The performance of environmental monitoring heavily depends on the availability of consecutive observation data and it turns out an increasing demand in remote sensing community for satellite image data in the sufficient resolution with respect to both spatial and temporal requirements, which appear to be conflictive and hard to tune tradeoffs. Multiple constellations could be a solution if without concerning cost, and thus it is so far interesting but very challenging to develop a method which can simultaneously improve both spatial and temporal details. There are some research efforts to deal with the problem from various aspects, a type of approaches is to enhance the spatial resolution using techniques of super resolution, pan-sharpen etc. which can produce good visual effects, but mostly cannot preserve spectral signatures and result in losing analytical value. Another type is to fill temporal frequency gaps by adopting time interpolation, which actually doesn't increase informative context at all. In this paper we presented a novel method to generate satellite images in higher spatial and temporal details, which further enables satellite image time series simulation. Our method starts with a pair of high-low resolution data set, and then a spatial registration is done by introducing LDA model to map high and low resolution pixels correspondingly. Afterwards, temporal change information is captured through a comparison of low resolution time series data, and the temporal change is then projected onto high resolution data plane and assigned to each high resolution pixel referring the predefined temporal change patterns of each type of ground objects to generate a simulated high resolution data. A preliminary experiment shows that our method can simulate a high resolution data with a good accuracy. We consider the contribution of our method is to enable timely monitoring of temporal changes through analysis of low resolution images time series only, and usage of costly high resolution data can be reduced as much as possible, and it presents an efficient solution with great cost performance to build up an economically operational monitoring service for environment, agriculture, forest, land use investigation, and other applications.

Agricultural Land Use mapping by multi-sensor approach for hydrological water quality monitoring

NASA Astrophysics Data System (ADS)

Brodsky, Lukas; Kodesova, Radka; Kodes, Vit

2010-05-01

The main objective of this study is to demonstrate potential of operational use of the high and medium resolution remote sensing data for hydrological water quality monitoring by mapping agriculture intensity and crop structures. In particular use of remote sensing mapping for optimization of pesticide monitoring. The agricultural mapping task is tackled by means of medium spatial and high temporal resolution ESA Envisat MERIS FR images together with single high spatial resolution IRS AWiFS image covering the whole area of interest (the Czech Republic). High resolution data (e.g. SPOT, ALOS, Landsat) are often used for agricultural land use classification, but usually only at regional or local level due to data availability and financial constraints. AWiFS data (nominal spatial resolution 56 m) due to the wide satellite swath seems to be more suitable for use at national level. Nevertheless, one of the critical issues for such a classification is to have sufficient image acquisitions over the whole vegetation period to describe crop development in appropriate way. ESA MERIS middle-resolution data were used in several studies for crop classification. The high temporal and also spectral resolution of MERIS data has indisputable advantage for crop classification. However, spatial resolution of 300 m results in mixture signal in a single pixel. AWiFS-MERIS data synergy brings new perspectives in agricultural Land Use mapping. Also, the developed methodology procedure is fully compatible with future use of ESA (GMES) Sentinel satellite images. The applied methodology of hybrid multi-sensor approach consists of these main stages: a/ parcel segmentation and spectral pre-classification of high resolution image (AWiFS); b/ ingestion of middle resolution (MERIS) vegetation spectro-temporal features; c/ vegetation signatures unmixing; and d/ semantic object-oriented classification of vegetation classes into final classification scheme. These crop groups were selected to be classified: winter crops, spring crops, oilseed rape, legumes, summer and other crops. This study highlights operational potentials of high temporal full resolution MERIS images in agricultural land use monitoring. Practical application of this methodology is foreseen, among others, in the water quality monitoring. Effective pesticide monitoring relies also on spatial distribution of applied pesticides, which can be derived from crop - plant protection product relationship. Knowledge of areas with predominant occurrence of specific crop based on remote sensing data described above can be used for a forecast of probable plant protection product application, thus cost-effective pesticide monitoring. The remote sensing data used on a continuous basis can be used in other long-term water management issues and provide valuable data for decision makers. Acknowledgement: Authors acknowledge the financial support of the Ministry of Education, Youth and Sports of the Czech Republic (grants No. 2B06095 and No. MSM 6046070901). The study was also supported by ESA CAT-1 (ref. 4358) and SOSI projects (Spatial Observation Services and Infrastructure; ref. GSTP-RTDA-EOPG-SW-08-0004).

Resolution enhancement in integral microscopy by physical interpolation.

PubMed

Llavador, Anabel; Sánchez-Ortiga, Emilio; Barreiro, Juan Carlos; Saavedra, Genaro; Martínez-Corral, Manuel

2015-08-01

Integral-imaging technology has demonstrated its capability for computing depth images from the microimages recorded after a single shot. This capability has been shown in macroscopic imaging and also in microscopy. Despite the possibility of refocusing different planes from one snap-shot is crucial for the study of some biological processes, the main drawback in integral imaging is the substantial reduction of the spatial resolution. In this contribution we report a technique, which permits to increase the two-dimensional spatial resolution of the computed depth images in integral microscopy by a factor of √2. This is made by a double-shot approach, carried out by means of a rotating glass plate, which shifts the microimages in the sensor plane. We experimentally validate the resolution enhancement as well as we show the benefit of applying the technique to biological specimens.

Resolution enhancement in integral microscopy by physical interpolation

PubMed Central

Llavador, Anabel; Sánchez-Ortiga, Emilio; Barreiro, Juan Carlos; Saavedra, Genaro; Martínez-Corral, Manuel

2015-01-01

Integral-imaging technology has demonstrated its capability for computing depth images from the microimages recorded after a single shot. This capability has been shown in macroscopic imaging and also in microscopy. Despite the possibility of refocusing different planes from one snap-shot is crucial for the study of some biological processes, the main drawback in integral imaging is the substantial reduction of the spatial resolution. In this contribution we report a technique, which permits to increase the two-dimensional spatial resolution of the computed depth images in integral microscopy by a factor of √2. This is made by a double-shot approach, carried out by means of a rotating glass plate, which shifts the microimages in the sensor plane. We experimentally validate the resolution enhancement as well as we show the benefit of applying the technique to biological specimens. PMID:26309749

Rapid mapping of polarization switching through complete information acquisition

DOE PAGES

Somnath, Suhas; Belianinov, Alex; Kalinin, Sergei V.; ...

2016-12-02

Polarization switching in ferroelectric and multiferroic materials underpins a broad range of current and emergent applications, ranging from random access memories to field-effect transistors, and tunnelling devices. Switching in these materials is exquisitely sensitive to local defects and microstructure on the nanometre scale, necessitating spatially resolved high-resolution studies of these phenomena. Classical piezoresponse force microscopy and spectroscopy, although providing necessary spatial resolution, are fundamentally limited in data acquisition rates and energy resolution. This limitation stems from their two-tiered measurement protocol that combines slow (~1 s) switching and fast (~10 kHz–1 MHz) detection waveforms. Here we develop an approach for rapidmore » probing of ferroelectric switching using direct strain detection of material response to probe bias. This approach, facilitated by high-sensitivity electronics and adaptive filtering, enables spectroscopic imaging at a rate 3,504 times faster the current state of the art, achieving high-veracity imaging of polarization dynamics in complex microstructures.« less

Rapid mapping of polarization switching through complete information acquisition

PubMed Central

Somnath, Suhas; Belianinov, Alex; Kalinin, Sergei V.; Jesse, Stephen

2016-01-01

Polarization switching in ferroelectric and multiferroic materials underpins a broad range of current and emergent applications, ranging from random access memories to field-effect transistors, and tunnelling devices. Switching in these materials is exquisitely sensitive to local defects and microstructure on the nanometre scale, necessitating spatially resolved high-resolution studies of these phenomena. Classical piezoresponse force microscopy and spectroscopy, although providing necessary spatial resolution, are fundamentally limited in data acquisition rates and energy resolution. This limitation stems from their two-tiered measurement protocol that combines slow (∼1 s) switching and fast (∼10 kHz–1 MHz) detection waveforms. Here we develop an approach for rapid probing of ferroelectric switching using direct strain detection of material response to probe bias. This approach, facilitated by high-sensitivity electronics and adaptive filtering, enables spectroscopic imaging at a rate 3,504 times faster the current state of the art, achieving high-veracity imaging of polarization dynamics in complex microstructures. PMID:27910941

Estimation of the high-spatial-resolution variability in extreme wind speeds for forestry applications

NASA Astrophysics Data System (ADS)

Venäläinen, Ari; Laapas, Mikko; Pirinen, Pentti; Horttanainen, Matti; Hyvönen, Reijo; Lehtonen, Ilari; Junila, Päivi; Hou, Meiting; Peltola, Heli M.

2017-07-01

The bioeconomy has an increasing role to play in climate change mitigation and the sustainable development of national economies. In Finland, a forested country, over 50 % of the current bioeconomy relies on the sustainable management and utilization of forest resources. Wind storms are a major risk that forests are exposed to and high-spatial-resolution analysis of the most vulnerable locations can produce risk assessment of forest management planning. In this paper, we examine the feasibility of the wind multiplier approach for downscaling of maximum wind speed, using 20 m spatial resolution CORINE land-use dataset and high-resolution digital elevation data. A coarse spatial resolution estimate of the 10-year return level of maximum wind speed was obtained from the ERA-Interim reanalyzed data. Using a geospatial re-mapping technique the data were downscaled to 26 meteorological station locations to represent very diverse environments. Applying a comparison, we find that the downscaled 10-year return levels represent 66 % of the observed variation among the stations examined. In addition, the spatial variation in wind-multiplier-downscaled 10-year return level wind was compared with the WAsP model-simulated wind. The heterogeneous test area was situated in northern Finland, and it was found that the major features of the spatial variation were similar, but in some locations, there were relatively large differences. The results indicate that the wind multiplier method offers a pragmatic and computationally feasible tool for identifying at a high spatial resolution those locations with the highest forest wind damage risks. It can also be used to provide the necessary wind climate information for wind damage risk model calculations, thus making it possible to estimate the probability of predicted threshold wind speeds for wind damage and consequently the probability (and amount) of wind damage for certain forest stand configurations.

Prevalence of pure versus mixed snow cover pixels across spatial resolutions in alpine environments: implications for binary and fractional remote sensing approaches

USGS Publications Warehouse

Selkowitz, David J.; Forster, Richard; Caldwell, Megan K.

2014-01-01

Remote sensing of snow-covered area (SCA) can be binary (indicating the presence/absence of snow cover at each pixel) or fractional (indicating the fraction of each pixel covered by snow). Fractional SCA mapping provides more information than binary SCA, but is more difficult to implement and may not be feasible with all types of remote sensing data. The utility of fractional SCA mapping relative to binary SCA mapping varies with the intended application as well as by spatial resolution, temporal resolution and period of interest, and climate. We quantified the frequency of occurrence of partially snow-covered (mixed) pixels at spatial resolutions between 1 m and 500 m over five dates at two study areas in the western U.S., using 0.5 m binary SCA maps derived from high spatial resolution imagery aggregated to fractional SCA at coarser spatial resolutions. In addition, we used in situ monitoring to estimate the frequency of partially snow-covered conditions for the period September 2013–August 2014 at 10 60-m grid cell footprints at two study areas with continental snow climates. Results from the image analysis indicate that at 40 m, slightly above the nominal spatial resolution of Landsat, mixed pixels accounted for 25%–93% of total pixels, while at 500 m, the nominal spatial resolution of MODIS bands used for snow cover mapping, mixed pixels accounted for 67%–100% of total pixels. Mixed pixels occurred more commonly at the continental snow climate site than at the maritime snow climate site. The in situ data indicate that some snow cover was present between 186 and 303 days, and partial snow cover conditions occurred on 10%–98% of days with snow cover. Four sites remained partially snow-free throughout most of the winter and spring, while six sites were entirely snow covered throughout most or all of the winter and spring. Within 60 m grid cells, the late spring/summer transition from snow-covered to snow-free conditions lasted 17–56 days and averaged 37 days. Our results suggest that mixed snow-covered snow-free pixels are common at the spatial resolutions imaged by both the Landsat and MODIS sensors. This highlights the additional information available from fractional SCA products and suggests fractional SCA can provide a major advantage for hydrological and climatological monitoring and modeling, particularly when accurate representation of the spatial distribution of snow cover is critical.

A parametric approach for simultaneous bias correction and high-resolution downscaling of climate model rainfall

NASA Astrophysics Data System (ADS)

Mamalakis, Antonios; Langousis, Andreas; Deidda, Roberto; Marrocu, Marino

2017-03-01

Distribution mapping has been identified as the most efficient approach to bias-correct climate model rainfall, while reproducing its statistics at spatial and temporal resolutions suitable to run hydrologic models. Yet its implementation based on empirical distributions derived from control samples (referred to as nonparametric distribution mapping) makes the method's performance sensitive to sample length variations, the presence of outliers, the spatial resolution of climate model results, and may lead to biases, especially in extreme rainfall estimation. To address these shortcomings, we propose a methodology for simultaneous bias correction and high-resolution downscaling of climate model rainfall products that uses: (a) a two-component theoretical distribution model (i.e., a generalized Pareto (GP) model for rainfall intensities above a specified threshold u*, and an exponential model for lower rainrates), and (b) proper interpolation of the corresponding distribution parameters on a user-defined high-resolution grid, using kriging for uncertain data. We assess the performance of the suggested parametric approach relative to the nonparametric one, using daily raingauge measurements from a dense network in the island of Sardinia (Italy), and rainfall data from four GCM/RCM model chains of the ENSEMBLES project. The obtained results shed light on the competitive advantages of the parametric approach, which is proved more accurate and considerably less sensitive to the characteristics of the calibration period, independent of the GCM/RCM combination used. This is especially the case for extreme rainfall estimation, where the GP assumption allows for more accurate and robust estimates, also beyond the range of the available data.

The Orbiting Carbon Observatory Mission: Watching the Earth Breathe Mapping CO2 from Space

NASA Technical Reports Server (NTRS)

Boain, Ron

2007-01-01

Approach: Collect spatially resolved, high resolution spectroscopic observations of CO2 and O2 absorption in reflected sunlight. Use these data to resolve spatial and temporal variations in the column averaged CO2 dry air mole fraction, X(sub CO2) over the sunlit hemisphere. Employ independent calibration and validation approaches to produce X(sub CO2) estimates with random errors and biases no larger than 1-2 ppm (0.3-0.5%) on regional scales at monthly intervals.

Methods and apparatus of spatially resolved electroluminescence of operating organic light-emitting diodes using conductive atomic force microscopy

NASA Technical Reports Server (NTRS)

Hersam, Mark C. (Inventor); Pingree, Liam S. C. (Inventor)

2008-01-01

A conductive atomic force microscopy (cAFM) technique which can concurrently monitor topography, charge transport, and electroluminescence with nanometer spatial resolution. This cAFM approach is particularly well suited for probing the electroluminescent response characteristics of operating organic light-emitting diodes (OLEDs) over short length scales.

Single-Image Super Resolution for Multispectral Remote Sensing Data Using Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Liebel, L.; Körner, M.

2016-06-01

In optical remote sensing, spatial resolution of images is crucial for numerous applications. Space-borne systems are most likely to be affected by a lack of spatial resolution, due to their natural disadvantage of a large distance between the sensor and the sensed object. Thus, methods for single-image super resolution are desirable to exceed the limits of the sensor. Apart from assisting visual inspection of datasets, post-processing operations—e.g., segmentation or feature extraction—can benefit from detailed and distinguishable structures. In this paper, we show that recently introduced state-of-the-art approaches for single-image super resolution of conventional photographs, making use of deep learning techniques, such as convolutional neural networks (CNN), can successfully be applied to remote sensing data. With a huge amount of training data available, end-to-end learning is reasonably easy to apply and can achieve results unattainable using conventional handcrafted algorithms. We trained our CNN on a specifically designed, domain-specific dataset, in order to take into account the special characteristics of multispectral remote sensing data. This dataset consists of publicly available SENTINEL-2 images featuring 13 spectral bands, a ground resolution of up to 10m, and a high radiometric resolution and thus satisfying our requirements in terms of quality and quantity. In experiments, we obtained results superior compared to competing approaches trained on generic image sets, which failed to reasonably scale satellite images with a high radiometric resolution, as well as conventional interpolation methods.

Near-field electromagnetic holography for high-resolution analysis of network interactions in neuronal tissue

PubMed Central

Kjeldsen, Henrik D.; Kaiser, Marcus; Whittington, Miles A.

2015-01-01

Background Brain function is dependent upon the concerted, dynamical interactions between a great many neurons distributed over many cortical subregions. Current methods of quantifying such interactions are limited by consideration only of single direct or indirect measures of a subsample of all neuronal population activity. New method Here we present a new derivation of the electromagnetic analogy to near-field acoustic holography allowing high-resolution, vectored estimates of interactions between sources of electromagnetic activity that significantly improves this situation. In vitro voltage potential recordings were used to estimate pseudo-electromagnetic energy flow vector fields, current and energy source densities and energy dissipation in reconstruction planes at depth into the neural tissue parallel to the recording plane of the microelectrode array. Results The properties of the reconstructed near-field estimate allowed both the utilization of super-resolution techniques to increase the imaging resolution beyond that of the microelectrode array, and facilitated a novel approach to estimating causal relationships between activity in neocortical subregions. Comparison with existing methods The holographic nature of the reconstruction method allowed significantly better estimation of the fine spatiotemporal detail of neuronal population activity, compared with interpolation alone, beyond the spatial resolution of the electrode arrays used. Pseudo-energy flow vector mapping was possible with high temporal precision, allowing a near-realtime estimate of causal interaction dynamics. Conclusions Basic near-field electromagnetic holography provides a powerful means to increase spatial resolution from electrode array data with careful choice of spatial filters and distance to reconstruction plane. More detailed approaches may provide the ability to volumetrically reconstruct activity patterns on neuronal tissue, but the ability to extract vectored data with the method presented already permits the study of dynamic causal interactions without bias from any prior assumptions on anatomical connectivity. PMID:26026581

Near-field electromagnetic holography for high-resolution analysis of network interactions in neuronal tissue.

PubMed

Kjeldsen, Henrik D; Kaiser, Marcus; Whittington, Miles A

2015-09-30

Brain function is dependent upon the concerted, dynamical interactions between a great many neurons distributed over many cortical subregions. Current methods of quantifying such interactions are limited by consideration only of single direct or indirect measures of a subsample of all neuronal population activity. Here we present a new derivation of the electromagnetic analogy to near-field acoustic holography allowing high-resolution, vectored estimates of interactions between sources of electromagnetic activity that significantly improves this situation. In vitro voltage potential recordings were used to estimate pseudo-electromagnetic energy flow vector fields, current and energy source densities and energy dissipation in reconstruction planes at depth into the neural tissue parallel to the recording plane of the microelectrode array. The properties of the reconstructed near-field estimate allowed both the utilization of super-resolution techniques to increase the imaging resolution beyond that of the microelectrode array, and facilitated a novel approach to estimating causal relationships between activity in neocortical subregions. The holographic nature of the reconstruction method allowed significantly better estimation of the fine spatiotemporal detail of neuronal population activity, compared with interpolation alone, beyond the spatial resolution of the electrode arrays used. Pseudo-energy flow vector mapping was possible with high temporal precision, allowing a near-realtime estimate of causal interaction dynamics. Basic near-field electromagnetic holography provides a powerful means to increase spatial resolution from electrode array data with careful choice of spatial filters and distance to reconstruction plane. More detailed approaches may provide the ability to volumetrically reconstruct activity patterns on neuronal tissue, but the ability to extract vectored data with the method presented already permits the study of dynamic causal interactions without bias from any prior assumptions on anatomical connectivity. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Crop Phenology Detection Using High Spatio-Temporal Resolution Data Fused from SPOT5 and MODIS Products

PubMed Central

Zheng, Yang; Wu, Bingfang; Zhang, Miao; Zeng, Hongwei

2016-01-01

Timely and efficient monitoring of crop phenology at a high spatial resolution are crucial for the precise and effective management of agriculture. Recently, satellite-derived vegetation indices (VIs), such as the Normalized Difference Vegetation Index (NDVI), have been widely used for the phenology detection of terrestrial ecosystems. In this paper, a framework is proposed to detect crop phenology using high spatio-temporal resolution data fused from Systeme Probatoire d'Observation de la Tarre5 (SPOT5) and Moderate Resolution Imaging Spectroradiometer (MODIS) images. The framework consists of a data fusion method to produce a synthetic NDVI dataset at SPOT5’s spatial resolution and at MODIS’s temporal resolution and a phenology extraction algorithm based on NDVI time-series analysis. The feasibility of our phenology detection approach was evaluated at the county scale in Shandong Province, China. The results show that (1) the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm can accurately blend SPOT5 and MODIS NDVI, with an R2 of greater than 0.69 and an root mean square error (RMSE) of less than 0.11 between the predicted and referenced data; and that (2) the estimated phenology parameters, such as the start and end of season (SOS and EOS), were closely correlated with the field-observed data with an R2 of the SOS ranging from 0.68 to 0.86 and with an R2 of the EOS ranging from 0.72 to 0.79. Our research provides a reliable approach for crop phenology mapping in areas with high fragmented farmland, which is meaningful for the implementation of precision agriculture. PMID:27973404

Crop Phenology Detection Using High Spatio-Temporal Resolution Data Fused from SPOT5 and MODIS Products.

PubMed

Zheng, Yang; Wu, Bingfang; Zhang, Miao; Zeng, Hongwei

2016-12-10

Timely and efficient monitoring of crop phenology at a high spatial resolution are crucial for the precise and effective management of agriculture. Recently, satellite-derived vegetation indices (VIs), such as the Normalized Difference Vegetation Index (NDVI), have been widely used for the phenology detection of terrestrial ecosystems. In this paper, a framework is proposed to detect crop phenology using high spatio-temporal resolution data fused from Systeme Probatoire d'Observation de la Tarre5 (SPOT5) and Moderate Resolution Imaging Spectroradiometer (MODIS) images. The framework consists of a data fusion method to produce a synthetic NDVI dataset at SPOT5's spatial resolution and at MODIS's temporal resolution and a phenology extraction algorithm based on NDVI time-series analysis. The feasibility of our phenology detection approach was evaluated at the county scale in Shandong Province, China. The results show that (1) the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm can accurately blend SPOT5 and MODIS NDVI, with an R ² of greater than 0.69 and an root mean square error (RMSE) of less than 0.11 between the predicted and referenced data; and that (2) the estimated phenology parameters, such as the start and end of season (SOS and EOS), were closely correlated with the field-observed data with an R ² of the SOS ranging from 0.68 to 0.86 and with an R ² of the EOS ranging from 0.72 to 0.79. Our research provides a reliable approach for crop phenology mapping in areas with high fragmented farmland, which is meaningful for the implementation of precision agriculture.

Cumulus cloud base height estimation from high spatial resolution Landsat data - A Hough transform approach

NASA Technical Reports Server (NTRS)

Berendes, Todd; Sengupta, Sailes K.; Welch, Ron M.; Wielicki, Bruce A.; Navar, Murgesh

1992-01-01

A semiautomated methodology is developed for estimating cumulus cloud base heights on the basis of high spatial resolution Landsat MSS data, using various image-processing techniques to match cloud edges with their corresponding shadow edges. The cloud base height is then estimated by computing the separation distance between the corresponding generalized Hough transform reference points. The differences between the cloud base heights computed by these means and a manual verification technique are of the order of 100 m or less; accuracies of 50-70 m may soon be possible via EOS instruments.

Least-squares model-based halftoning

NASA Astrophysics Data System (ADS)

Pappas, Thrasyvoulos N.; Neuhoff, David L.

1992-08-01

A least-squares model-based approach to digital halftoning is proposed. It exploits both a printer model and a model for visual perception. It attempts to produce an 'optimal' halftoned reproduction, by minimizing the squared error between the response of the cascade of the printer and visual models to the binary image and the response of the visual model to the original gray-scale image. Conventional methods, such as clustered ordered dither, use the properties of the eye only implicitly, and resist printer distortions at the expense of spatial and gray-scale resolution. In previous work we showed that our printer model can be used to modify error diffusion to account for printer distortions. The modified error diffusion algorithm has better spatial and gray-scale resolution than conventional techniques, but produces some well known artifacts and asymmetries because it does not make use of an explicit eye model. Least-squares model-based halftoning uses explicit eye models and relies on printer models that predict distortions and exploit them to increase, rather than decrease, both spatial and gray-scale resolution. We have shown that the one-dimensional least-squares problem, in which each row or column of the image is halftoned independently, can be implemented with the Viterbi's algorithm. Unfortunately, no closed form solution can be found in two dimensions. The two-dimensional least squares solution is obtained by iterative techniques. Experiments show that least-squares model-based halftoning produces more gray levels and better spatial resolution than conventional techniques. We also show that the least- squares approach eliminates the problems associated with error diffusion. Model-based halftoning can be especially useful in transmission of high quality documents using high fidelity gray-scale image encoders. As we have shown, in such cases halftoning can be performed at the receiver, just before printing. Apart from coding efficiency, this approach permits the halftoner to be tuned to the individual printer, whose characteristics may vary considerably from those of other printers, for example, write-black vs. write-white laser printers.

Long-term, high-spatial resolution carbon balance monitoring of the Amazonian frontier: Predisturbance and postdisturbance carbon emissions and uptake

NASA Astrophysics Data System (ADS)

Toomey, Michael; Roberts, Dar A.; Caviglia-Harris, Jill; Cochrane, Mark A.; Dewes, Candida F.; Harris, Daniel; Numata, Izaya; Sales, Marcio H.; Sills, Erin; Souza, Carlos M.

2013-06-01

We performed high-spatial and high-temporal resolution modeling of carbon stocks and fluxes in the state of Rondônia, Brazil for the period 1985-2009, using annual Landsat-derived land cover classifications and a modified bookkeeping modeling approach. According to these results, Rondônia contributed 3.5-4% of pantropical humid forest deforestation emissions over this period. Similar to well-known figures reported by the Brazilian Space Agency, we found a decline in deforestation rates since 2006. However, we estimate a lesser decrease, with deforestation rates continuing at levels similar to the early 2000s. Forest carbon stocks declined at an annual rate of 1.51%; emissions from postdisturbance land use nearly equaled those of the initial deforestation events. Carbon uptake by secondary forest was negligible due to limited spatial extent and high turnover rates. Net carbon emissions represented 93% of initial forest carbon stocks, due in part to repeated slash and pasture burnings and secondary forest clearing. We analyzed potential error incurred when spatially aggregating land cover by comparing results based on coarser-resolution (250 m) and full-resolution land cover products. At the coarser resolution, more than 90% of deforestation and secondary forest would be unresolvable, assuming that a 50% change threshold is necessary for detection. Therefore, we strongly suggest the use of Landsat-scale ( 30m) resolution carbon monitoring in tropical regions dominated by nonmechanized, smallholder land use change.

4D electron microscopy: principles and applications.

PubMed

Flannigan, David J; Zewail, Ahmed H

2012-10-16

The transmission electron microscope (TEM) is a powerful tool enabling the visualization of atoms with length scales smaller than the Bohr radius at a factor of only 20 larger than the relativistic electron wavelength of 2.5 pm at 200 keV. The ability to visualize matter at these scales in a TEM is largely due to the efforts made in correcting for the imperfections in the lens systems which introduce aberrations and ultimately limit the achievable spatial resolution. In addition to the progress made in increasing the spatial resolution, the TEM has become an all-in-one characterization tool. Indeed, most of the properties of a material can be directly mapped in the TEM, including the composition, structure, bonding, morphology, and defects. The scope of applications spans essentially all of the physical sciences and includes biology. Until recently, however, high resolution visualization of structural changes occurring on sub-millisecond time scales was not possible. In order to reach the ultrashort temporal domain within which fundamental atomic motions take place, while simultaneously retaining high spatial resolution, an entirely new approach from that of millisecond-limited TEM cameras had to be conceived. As shown below, the approach is also different from that of nanosecond-limited TEM, whose resolution cannot offer the ultrafast regimes of dynamics. For this reason "ultrafast electron microscopy" is reserved for the field which is concerned with femtosecond to picosecond resolution capability of structural dynamics. In conventional TEMs, electrons are produced by heating a source or by applying a strong extraction field. Both methods result in the stochastic emission of electrons, with no control over temporal spacing or relative arrival time at the specimen. The timing issue can be overcome by exploiting the photoelectric effect and using pulsed lasers to generate precisely timed electron packets of ultrashort duration. The spatial and temporal resolutions achievable with short intense pulses containing a large number of electrons, however, are limited to tens of nanometers and nanoseconds, respectively. This is because Coulomb repulsion is significant in such a pulse, and the electrons spread in space and time, thus limiting the beam coherence. It is therefore not possible to image the ultrafast elementary dynamics of complex transformations. The challenge was to retain the high spatial resolution of a conventional TEM while simultaneously enabling the temporal resolution required to visualize atomic-scale motions. In this Account, we discuss the development of four-dimensional ultrafast electron microscopy (4D UEM) and summarize techniques and applications that illustrate the power of the approach. In UEM, images are obtained either stroboscopically with coherent single-electron packets or with a single electron bunch. Coulomb repulsion is absent under the single-electron condition, thus permitting imaging, diffraction, and spectroscopy, all with high spatiotemporal resolution, the atomic scale (sub-nanometer and femtosecond). The time resolution is limited only by the laser pulse duration and energy carried by the electron packets; the CCD camera has no bearing on the temporal resolution. In the regime of single pulses of electrons, the temporal resolution of picoseconds can be attained when hundreds of electrons are in the bunch. The applications given here are selected to highlight phenomena of different length and time scales, from atomic motions during structural dynamics to phase transitions and nanomechanical oscillations. We conclude with a brief discussion of emerging methods, which include scanning ultrafast electron microscopy (S-UEM), scanning transmission ultrafast electron microscopy (ST-UEM) with convergent beams, and time-resolved imaging of biological structures at ambient conditions with environmental cells.

Downscaling remotely sensed imagery using area-to-point cokriging and multiple-point geostatistical simulation

NASA Astrophysics Data System (ADS)

Tang, Yunwei; Atkinson, Peter M.; Zhang, Jingxiong

2015-03-01

A cross-scale data integration method was developed and tested based on the theory of geostatistics and multiple-point geostatistics (MPG). The goal was to downscale remotely sensed images while retaining spatial structure by integrating images at different spatial resolutions. During the process of downscaling, a rich spatial correlation model in the form of a training image was incorporated to facilitate reproduction of similar local patterns in the simulated images. Area-to-point cokriging (ATPCK) was used as locally varying mean (LVM) (i.e., soft data) to deal with the change of support problem (COSP) for cross-scale integration, which MPG cannot achieve alone. Several pairs of spectral bands of remotely sensed images were tested for integration within different cross-scale case studies. The experiment shows that MPG can restore the spatial structure of the image at a fine spatial resolution given the training image and conditioning data. The super-resolution image can be predicted using the proposed method, which cannot be realised using most data integration methods. The results show that ATPCK-MPG approach can achieve greater accuracy than methods which do not account for the change of support issue.

Mapping wood density globally using remote sensing and climatological data

NASA Astrophysics Data System (ADS)

Moreno, A.; Camps-Valls, G.; Carvalhais, N.; Kattge, J.; Robinson, N.; Reichstein, M.; Allred, B. W.; Running, S. W.

2017-12-01

Wood density (WD) is defined as the oven-dry mass divided by fresh volume, varies between individuals, and describes the carbon investment per unit volume of stem. WD has been proven to be a key functional trait in carbon cycle research and correlates with numerous morphological, mechanical, physiological, and ecological properties. In spite of the utility and importance of this trait, there is a lack of an operational framework to spatialize plant WD measurements at a global scale. In this work, we present a consistent modular processing chain to derive global maps (500 m) of WD using modern machine learning techniques along with optical remote sensing data (MODIS/Landsat) and climate data using the Google Earth Engine platform. The developed approach uses a hierarchical Bayesian approach to fill in gaps in the plant measured WD data set to maximize its global representativeness. WD plant species are then aggregated to Plant Functional Types (PFT). The spatial abundance of PFT at 500 m spatial resolution (MODIS) is calculated using a high resolution (30 m) PFT map developed using Landsat data. Based on these PFT abundances, representative WD values are estimated for each MODIS pixel with nearby measured data. Finally, random forests are used to globally estimate WD from these MODIS pixels using remote sensing and climate. The validation and assessment of the applied methods indicate that the model explains more than 72% of the spatial variance of the calculated community aggregated WD estimates with virtually unbiased estimates and low RMSE (<15%). The maps thus offer new opportunities to study and analyze the global patterns of variation of WD at an unprecedented spatial coverage and spatial resolution.

Rainfall disaggregation for urban hydrology: Effects of spatial consistence

NASA Astrophysics Data System (ADS)

Müller, Hannes; Haberlandt, Uwe

2015-04-01

For urban hydrology rainfall time series with a high temporal resolution are crucial. Observed time series of this kind are very short in most cases, so they cannot be used. On the contrary, time series with lower temporal resolution (daily measurements) exist for much longer periods. The objective is to derive time series with a long duration and a high resolution by disaggregating time series of the non-recording stations with information of time series of the recording stations. The multiplicative random cascade model is a well-known disaggregation model for daily time series. For urban hydrology it is often assumed, that a day consists of only 1280 minutes in total as starting point for the disaggregation process. We introduce a new variant for the cascade model, which is functional without this assumption and also outperforms the existing approach regarding time series characteristics like wet and dry spell duration, average intensity, fraction of dry intervals and extreme value representation. However, in both approaches rainfall time series of different stations are disaggregated without consideration of surrounding stations. This yields in unrealistic spatial patterns of rainfall. We apply a simulated annealing algorithm that has been used successfully for hourly values before. Relative diurnal cycles of the disaggregated time series are resampled to reproduce the spatial dependence of rainfall. To describe spatial dependence we use bivariate characteristics like probability of occurrence, continuity ratio and coefficient of correlation. Investigation area is a sewage system in Northern Germany. We show that the algorithm has the capability to improve spatial dependence. The influence of the chosen disaggregation routine and the spatial dependence on overflow occurrences and volumes of the sewage system will be analyzed.

Spatial Angular Compounding Technique for H-Scan Ultrasound Imaging.

PubMed

Khairalseed, Mawia; Xiong, Fangyuan; Kim, Jung-Whan; Mattrey, Robert F; Parker, Kevin J; Hoyt, Kenneth

2018-01-01

H-Scan is a new ultrasound imaging technique that relies on matching a model of pulse-echo formation to the mathematics of a class of Gaussian-weighted Hermite polynomials. This technique may be beneficial in the measurement of relative scatterer sizes and in cancer therapy, particularly for early response to drug treatment. Because current H-scan techniques use focused ultrasound data acquisitions, spatial resolution degrades away from the focal region and inherently affects relative scatterer size estimation. Although the resolution of ultrasound plane wave imaging can be inferior to that of traditional focused ultrasound approaches, the former exhibits a homogeneous spatial resolution throughout the image plane. The purpose of this study was to implement H-scan using plane wave imaging and investigate the impact of spatial angular compounding on H-scan image quality. Parallel convolution filters using two different Gaussian-weighted Hermite polynomials that describe ultrasound scattering events are applied to the radiofrequency data. The H-scan processing is done on each radiofrequency image plane before averaging to get the angular compounded image. The relative strength from each convolution is color-coded to represent relative scatterer size. Given results from a series of phantom materials, H-scan imaging with spatial angular compounding more accurately reflects the true scatterer size caused by reductions in the system point spread function and improved signal-to-noise ratio. Preliminary in vivo H-scan imaging of tumor-bearing animals suggests this modality may be useful for monitoring early response to chemotherapeutic treatment. Overall, H-scan imaging using ultrasound plane waves and spatial angular compounding is a promising approach for visualizing the relative size and distribution of acoustic scattering sources. Copyright © 2018 World Federation for Ultrasound in Medicine and Biology. Published by Elsevier Inc. All rights reserved.

The Canadian Hydrological Model (CHM): A multi-scale, variable-complexity hydrological model for cold regions

NASA Astrophysics Data System (ADS)

Marsh, C.; Pomeroy, J. W.; Wheater, H. S.

2016-12-01

There is a need for hydrological land surface schemes that can link to atmospheric models, provide hydrological prediction at multiple scales and guide the development of multiple objective water predictive systems. Distributed raster-based models suffer from an overrepresentation of topography, leading to wasted computational effort that increases uncertainty due to greater numbers of parameters and initial conditions. The Canadian Hydrological Model (CHM) is a modular, multiphysics, spatially distributed modelling framework designed for representing hydrological processes, including those that operate in cold-regions. Unstructured meshes permit variable spatial resolution, allowing coarse resolutions at low spatial variability and fine resolutions as required. Model uncertainty is reduced by lessening the necessary computational elements relative to high-resolution rasters. CHM uses a novel multi-objective approach for unstructured triangular mesh generation that fulfills hydrologically important constraints (e.g., basin boundaries, water bodies, soil classification, land cover, elevation, and slope/aspect). This provides an efficient spatial representation of parameters and initial conditions, as well as well-formed and well-graded triangles that are suitable for numerical discretization. CHM uses high-quality open source libraries and high performance computing paradigms to provide a framework that allows for integrating current state-of-the-art process algorithms. The impact of changes to model structure, including individual algorithms, parameters, initial conditions, driving meteorology, and spatial/temporal discretization can be easily tested. Initial testing of CHM compared spatial scales and model complexity for a spring melt period at a sub-arctic mountain basin. The meshing algorithm reduced the total number of computational elements and preserved the spatial heterogeneity of predictions.

Development of ultrahigh resolution alpha particle imaging detector using 1 mm channel size Si-PM array

NASA Astrophysics Data System (ADS)

Yamamoto, Seiichi; Kawaguchi, Wataru

2018-06-01

For precise distribution measurements of alpha particles, a high-resolution alpha particle imaging detector is required. Although combining a thin scintillator with a silicon photomultiplier (Si-PM) array is a promising method for achieving high resolution, the spatial resolution is limited. Reducing the size of the Si-PM array is a possible approach to improving the spatial resolution of the alpha particle imaging detector. Consequently, we employed a 1 mm channel size Si-PM array combined with a thin ZnS(Ag) sheet to form an alpha particle imaging detector and evaluated the performance. For the developed alpha particle imaging detector, an Si-PM array with 1 mm x 1 mm channel size arranged 8 x 8 was optically coupled to a ZnS(Ag) sheet with a 1-mm-thick light guide between them. The size of the alpha particle imaging detector was 9.5 mm x 9.5 mm. The spatial resolution of the developed alpha particle imaging detector was 0.14 mm FWHM, and the energy resolution was 74% FWHM for 5.5 MeV alpha particles. The uniformity of the imaging detector at the central part of the field of view (FOV) was ±4.7%. The background count rate was 0.06 counts/min. We obtained various high-resolution phantom images for alpha particles with the developed system. We conclude that the developed imaging detector is promising for high-resolution distribution measurements of alpha particles.

Combining HJ CCD, GF-1 WFV and MODIS Data to Generate Daily High Spatial Resolution Synthetic Data for Environmental Process Monitoring.

PubMed

Wu, Mingquan; Huang, Wenjiang; Niu, Zheng; Wang, Changyao

2015-08-20

The limitations of satellite data acquisition mean that there is a lack of satellite data with high spatial and temporal resolutions for environmental process monitoring. In this study, we address this problem by applying the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and the Spatial and Temporal Data Fusion Approach (STDFA) to combine Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field of view camera (GF-1 WFV) and Moderate Resolution Imaging Spectroradiometer (MODIS) data to generate daily high spatial resolution synthetic data for land surface process monitoring. Actual HJ CCD and GF-1 WFV data were used to evaluate the precision of the synthetic images using the correlation analysis method. Our method was tested and validated for two study areas in Xinjiang Province, China. The results show that both the ESTARFM and STDFA can be applied to combine HJ CCD and MODIS reflectance data, and GF-1 WFV and MODIS reflectance data, to generate synthetic HJ CCD data and synthetic GF-1 WFV data that closely match actual data with correlation coefficients (r) greater than 0.8989 and 0.8643, respectively. Synthetic red- and near infrared (NIR)-band data generated by ESTARFM are more suitable for the calculation of Normalized Different Vegetation Index (NDVI) than the data generated by STDFA.

Combining HJ CCD, GF-1 WFV and MODIS Data to Generate Daily High Spatial Resolution Synthetic Data for Environmental Process Monitoring

PubMed Central

Wu, Mingquan; Huang, Wenjiang; Niu, Zheng; Wang, Changyao

2015-01-01

The limitations of satellite data acquisition mean that there is a lack of satellite data with high spatial and temporal resolutions for environmental process monitoring. In this study, we address this problem by applying the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and the Spatial and Temporal Data Fusion Approach (STDFA) to combine Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field of view camera (GF-1 WFV) and Moderate Resolution Imaging Spectroradiometer (MODIS) data to generate daily high spatial resolution synthetic data for land surface process monitoring. Actual HJ CCD and GF-1 WFV data were used to evaluate the precision of the synthetic images using the correlation analysis method. Our method was tested and validated for two study areas in Xinjiang Province, China. The results show that both the ESTARFM and STDFA can be applied to combine HJ CCD and MODIS reflectance data, and GF-1 WFV and MODIS reflectance data, to generate synthetic HJ CCD data and synthetic GF-1 WFV data that closely match actual data with correlation coefficients (r) greater than 0.8989 and 0.8643, respectively. Synthetic red- and near infrared (NIR)-band data generated by ESTARFM are more suitable for the calculation of Normalized Different Vegetation Index (NDVI) than the data generated by STDFA. PMID:26308017

A thin wideband high-spatial-resolution focusing metasurface for near-field passive millimeter-wave imaging

NASA Astrophysics Data System (ADS)

Chu, Hongjun; Qi, Jiaran; Xiao, Shanshan; Qiu, Jinghui

2018-04-01

In this paper, we present a flat transmission-type focusing metasurface for the near-field passive millimeter-wave (PMMW) imaging systems. Considering the non-uniform wavefront of the actual feeding horn, the metasurface is configured by unit cells consisting of coaxial annular apertures and is optimized to achieve broadband, high spatial resolution, and polarization insensitive properties important for PMMW imaging applications in the frequency range from 33 GHz to 37 GHz, with the focal spot as small as 0.43λ0 (@35 GHz). A prototype of the proposed metasurface is fabricated, and the measurement results fairly agree with the simulation ones. Furthermore, an experimental single-sensor PMMW imaging system is constructed based on the metasurface and a Ka-band direct detection radiometer. The experimental results show that the azimuth resolution of the system can reach approximately 4 mm (≈0.47λ0). It is shown that the proposed metasurface can potentially replace the bulky dielectric-lens or reflector antenna to achieve possibly more compact PMMW imaging systems with high spatial resolution approaching the diffraction-limit.

Depth-resolved mid-infrared photothermal imaging of living cells and organisms with submicrometer spatial resolution

PubMed Central

Zhang, Delong; Li, Chen; Zhang, Chi; Slipchenko, Mikhail N.; Eakins, Gregory; Cheng, Ji-Xin

2016-01-01

Chemical contrast has long been sought for label-free visualization of biomolecules and materials in complex living systems. Although infrared spectroscopic imaging has come a long way in this direction, it is thus far only applicable to dried tissues because of the strong infrared absorption by water. It also suffers from low spatial resolution due to long wavelengths and lacks optical sectioning capabilities. We overcome these limitations through sensing vibrational absorption–induced photothermal effect by a visible laser beam. Our mid-infrared photothermal (MIP) approach reached 10 μM detection sensitivity and submicrometer lateral spatial resolution. This performance has exceeded the diffraction limit of infrared microscopy and allowed label-free three-dimensional chemical imaging of live cells and organisms. Distributions of endogenous lipid and exogenous drug inside single cells were visualized. We further demonstrated in vivo MIP imaging of lipids and proteins in Caenorhabditis elegans. The reported MIP imaging technology promises broad applications from monitoring metabolic activities to high-resolution mapping of drug molecules in living systems, which are beyond the reach of current infrared microscopy. PMID:27704043

Depth-resolved mid-infrared photothermal imaging of living cells and organisms with submicrometer spatial resolution.

PubMed

Zhang, Delong; Li, Chen; Zhang, Chi; Slipchenko, Mikhail N; Eakins, Gregory; Cheng, Ji-Xin

2016-09-01

Chemical contrast has long been sought for label-free visualization of biomolecules and materials in complex living systems. Although infrared spectroscopic imaging has come a long way in this direction, it is thus far only applicable to dried tissues because of the strong infrared absorption by water. It also suffers from low spatial resolution due to long wavelengths and lacks optical sectioning capabilities. We overcome these limitations through sensing vibrational absorption-induced photothermal effect by a visible laser beam. Our mid-infrared photothermal (MIP) approach reached 10 μM detection sensitivity and submicrometer lateral spatial resolution. This performance has exceeded the diffraction limit of infrared microscopy and allowed label-free three-dimensional chemical imaging of live cells and organisms. Distributions of endogenous lipid and exogenous drug inside single cells were visualized. We further demonstrated in vivo MIP imaging of lipids and proteins in Caenorhabditis elegans . The reported MIP imaging technology promises broad applications from monitoring metabolic activities to high-resolution mapping of drug molecules in living systems, which are beyond the reach of current infrared microscopy.

Improving Secondary Ion Mass Spectrometry Image Quality with Image Fusion

NASA Astrophysics Data System (ADS)

Tarolli, Jay G.; Jackson, Lauren M.; Winograd, Nicholas

2014-12-01

The spatial resolution of chemical images acquired with cluster secondary ion mass spectrometry (SIMS) is limited not only by the size of the probe utilized to create the images but also by detection sensitivity. As the probe size is reduced to below 1 μm, for example, a low signal in each pixel limits lateral resolution because of counting statistics considerations. Although it can be useful to implement numerical methods to mitigate this problem, here we investigate the use of image fusion to combine information from scanning electron microscope (SEM) data with chemically resolved SIMS images. The advantage of this approach is that the higher intensity and, hence, spatial resolution of the electron images can help to improve the quality of the SIMS images without sacrificing chemical specificity. Using a pan-sharpening algorithm, the method is illustrated using synthetic data, experimental data acquired from a metallic grid sample, and experimental data acquired from a lawn of algae cells. The results show that up to an order of magnitude increase in spatial resolution is possible to achieve. A cross-correlation metric is utilized for evaluating the reliability of the procedure.

Enhancing Spatial Resolution of Remotely Sensed Imagery Using Deep Learning

NASA Astrophysics Data System (ADS)

Beck, J. M.; Bridges, S.; Collins, C.; Rushing, J.; Graves, S. J.

2017-12-01

Researchers at the Information Technology and Systems Center at the University of Alabama in Huntsville are using Deep Learning with Convolutional Neural Networks (CNNs) to develop a method for enhancing the spatial resolutions of moderate resolution (10-60m) multispectral satellite imagery. This enhancement will effectively match the resolutions of imagery from multiple sensors to provide increased global temporal-spatial coverage for a variety of Earth science products. Our research is centered on using Deep Learning for automatically generating transformations for increasing the spatial resolution of remotely sensed images with different spatial, spectral, and temporal resolutions. One of the most important steps in using images from multiple sensors is to transform the different image layers into the same spatial resolution, preferably the highest spatial resolution, without compromising the spectral information. Recent advances in Deep Learning have shown that CNNs can be used to effectively and efficiently upscale or enhance the spatial resolution of multispectral images with the use of an auxiliary data source such as a high spatial resolution panchromatic image. In contrast, we are using both the spatial and spectral details inherent in low spatial resolution multispectral images for image enhancement without the use of a panchromatic image. This presentation will discuss how this technology will benefit many Earth Science applications that use remotely sensed images with moderate spatial resolutions.

Applicability of Various Interpolation Approaches for High Resolution Spatial Mapping of Climate Data in Korea

NASA Astrophysics Data System (ADS)

Jo, A.; Ryu, J.; Chung, H.; Choi, Y.; Jeon, S.

2018-04-01

The purpose of this study is to create a new dataset of spatially interpolated monthly climate data for South Korea at high spatial resolution (approximately 30m) by performing various spatio-statistical interpolation and comparing with forecast LDAPS gridded climate data provided from Korea Meterological Administration (KMA). Automatic Weather System (AWS) and Automated Synoptic Observing System (ASOS) data in 2017 obtained from KMA were included for the spatial mapping of temperature and rainfall; instantaneous temperature and 1-hour accumulated precipitation at 09:00 am on 31th March, 21th June, 23th September, and 24th December. Among observation data, 80 percent of the total point (478) and remaining 120 points were used for interpolations and for quantification, respectively. With the training data and digital elevation model (DEM) with 30 m resolution, inverse distance weighting (IDW), co-kriging, and kriging were performed by using ArcGIS10.3.1 software and Python 3.6.4. Bias and root mean square were computed to compare prediction performance quantitatively. When statistical analysis was performed for each cluster using 20 % validation data, co kriging was more suitable for spatialization of instantaneous temperature than other interpolation method. On the other hand, IDW technique was appropriate for spatialization of precipitation.

Addressing spatial scales and new mechanisms in climate impact ecosystem modeling

NASA Astrophysics Data System (ADS)

Poulter, B.; Joetzjer, E.; Renwick, K.; Ogunkoya, G.; Emmett, K.

2015-12-01

Climate change impacts on vegetation distributions are typically addressed using either an empirical approach, such as a species distribution model (SDM), or with process-based methods, for example, dynamic global vegetation models (DGVMs). Each approach has its own benefits and disadvantages. For example, an SDM is constrained by data and few parameters, but does not include adaptation or acclimation processes or other ecosystem feedbacks that may act to mitigate or enhance climate effects. Alternatively, a DGVM model includes many mechanisms relating plant growth and disturbance to climate, but simulations are costly to perform at high-spatial resolution and there remains large uncertainty on a variety of fundamental physical processes. To address these issues, here, we present two DGVM-based case studies where i) high-resolution (1 km) simulations are being performed for vegetation in the Greater Yellowstone Ecosystem using a biogeochemical, forest gap model, LPJ-GUESS, and ii) where new mechanisms for simulating tropical tree-mortality are being introduced. High-resolution DGVM model simulations require not only computing and reorganizing code but also a consideration of scaling issues on vegetation dynamics and stochasticity and also on disturbance and migration. New mechanisms for simulating forest mortality must consider hydraulic limitations and carbon reserves and their interactions on source-sink dynamics and in controlling water potentials. Improving DGVM approaches by addressing spatial scale challenges and integrating new approaches for estimating forest mortality will provide new insights more relevant for land management and possibly reduce uncertainty by physical processes more directly comparable to experimental and observational evidence.

High-Resolution Forest Carbon Monitoring and Modeling: Continued Prototype Development and Deployment Across The Tri-state Area (MD, PA, DE), USA

NASA Astrophysics Data System (ADS)

Hurtt, G. C.; Birdsey, R.; Campbell, E.; Dolan, K. A.; Dubayah, R.; Escobar, V. M.; Finley, A. O.; Flanagan, S.; Huang, W.; Johnson, K.; Lister, A.; ONeil-Dunne, J.; Sepulveda Carlo, E.; Zhao, M.

2017-12-01

Local, national and international programs have increasing need for precise and accurate estimates of forest carbon and structure to support greenhouse gas reduction plans, climate initiatives, and other international climate treaty frameworks. In 2010 Congress directed NASA to initiate research towards the development of Carbon Monitoring Systems (CMS). In response, our team has worked to develop a robust, replicable framework to produce maps of high-resolution carbon stocks and future carbon sequestration potential. High-resolution (30m) maps of carbon stocks and uncertainty were produced by linking national 1m-resolution imagery and existing wall-to-wall airborne lidar to spatially explicit in-situ field observations such as the USFS Forest Inventory and Analysis (FIA) network. These same data, characterizing forest extent and vertical structure, were used to drive a prognostic ecosystem model to predict carbon fluxes and carbon sequestration potential at unprecedented spatial resolution and scale (90m), more than 100,000 times the spatial resolution of standard global models. Through project development, the domain of this research has expanded from two counties in MD (2,181 km2), to the entire state (32,133 km2), to the tri-state region of MD, PA, and DE (157,868 km2), covering forests in four major USDA ecological providences (Eastern Broadleaf, Northeastern Mixed, Outer Coastal Plain, and Central Appalachian). Across the region, we estimate 694 Tg C (14 DE, 113 MD, 567 PA) in above ground biomass, and estimate a carbon sequestration potential more than twice that amount. Empirical biomass products enhance existing approaches though high resolution accounting for trees outside traditional forest maps. Modeling products move beyond traditional MRV, and map future afforestation and reforestation potential for carbon at local actionable spatial scales. These products are relevant to multiple stakeholder needs in the region as discussed through the Tri-sate Working Group, and are actively being used to inform the state of MD's Greenhouse Gas Reduction Act. The approach is scalable, and provides a protoype framework for application in other domains and for future spaceborne lidar missions.

Phase division multiplexed EIT for enhanced temporal resolution.

PubMed

Dowrick, T; Holder, D

2018-03-29

The most commonly used EIT paradigm (time division multiplexing) limits the temporal resolution of impedance images due to the need to switch between injection electrodes. Advances have previously been made using frequency division multiplexing (FDM) to increase temporal resolution, but in cases where a fixed range of frequencies is available, such as imaging fast neural activity, an upper limit is placed on the total number of simultaneous injections. The use of phase division multiplexing (PDM) where multiple out of phase signals can be injected at each frequency is investigated to increase temporal resolution. TDM, FDM and PDM were compared in head tank experiments, to compare transfer impedance measurements and spatial resolution between the three techniques. A resistor phantom paradigm was established to investigate the imaging of one-off impedance changes, of magnitude 1% and with durations as low as 500 µs (similar to those seen in nerve bundles), using both PDM and TDM approaches. In head tank experiments, a strong correlation (r  >  0.85 and p  <  0.001) was present between the three sets of measured transfer impedances, and no statistically significant difference was found in reconstructed image quality. PDM was able to image impedance changes down to 500 µs in the phantom experiments, while the minimum duration imaged using TDM was 5 ms. PDM offers a possible solution to the imaging of fast moving impedance changes (such as in nerves), where the use of triggering or coherent averaging is not possible. The temporal resolution presents an order of magnitude improvement of the TDM approach, and the approach addresses the limited spatial resolution of FDM by increasing the number of simultaneous EIT injections.

WE-EF-303-08: Proton Radiography Using Pencil Beam Scanning and Novel Micromegas Detectors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dolney, D; Lustig, R; Teo, B

Purpose: While the energy of therapeutic proton beams can be adjusted to penetrate to any given depth in water, range uncertainties arise in patients due in part to imprecise knowledge of the stopping power of protons in human tissues. Proton radiography is one approach to reduce the beam range uncertainty, thereby allowing for a reduction in treatment margins and dose escalation. Methods: The authors have adapted a novel detector technology based on Micromesh Gaseous Structure (“Micromegas”) for proton therapy beams and have demonstrated fine spatial and time resolution of magnetically scanned proton pencil beams, as well as wide dynamic rangemore » for dosimetry. In this work, proton radiographs were obtained using Micromegas 2D planes positioned downstream of solid water assemblies. The position-sensitive monitor chambers in the IBA proton delivery nozzle provide the beam entrance position. Results: Radiography with Micromegas detectors and actively scanned beams provide spatial resolution of up to 300 µm and water-equivalent thickness (WET) resolution as good as 0.02% (60 µm out of 31 cm total thickness), with the dose delivered to the patient kept below 2 cGy. The spatial resolution as a function of sample rate and number of delivered protons is found to be near the theoretical Cramer-Rao lower bound. Using the CR bound, we argue that the imaging dose could be further lowered to 1 mGy, while still achieving sub-mm spatial resolution, by relatively simple instrumentation upgrades and beam delivery modifications. Conclusion: For proton radiography, high spatial and WET resolution can be achieved, with minimal additional dose to patient, by using magnetically scanned proton pencil beams and Micromegas detectors.« less

SU-C-207A-02: Proton Radiography Using Pencil Beam Scanning and a Novel, Low-Cost Range Telescope

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dolney, D; Mayers, G; Newcomer, M

Purpose: While the energy of therapeutic proton beams can be adjusted to penetrate to any given depth in water, range uncertainties arise in patients due in part to imprecise knowledge of the stopping power of protons in human tissues [1]. Proton radiography is one approach to reduce the beam range uncertainty [2], thereby allowing for a reduction in treatment margins and dose escalation. Methods: The authors have adapted a novel detector technology based on Micromesh Gaseous Structure (“Micromegas”) for proton therapy beams and have demonstrated fine spatial and time resolution of magnetically scanned proton pencil beams, as well as widemore » dynamic range for dosimetry [3]. The authors have constructed a prototype imaging system comprised of 5 Micromegas layers. Proton radiographs were obtained downstream of solid water assemblies. The position-sensitive monitor chambers in the IBA proton delivery nozzle provide the beam entrance position. Results: Our technique achieves spatial resolution as low as 300 µm and water-equivalent thickness (WET) resolution as good as 0.02% (60 µm out of 31 cm total thickness). The dose delivered to the patient is kept below 2 cGy. The spatial resolution as a function of sample rate and number of delivered protons is found to be near the theoretical Cramer-Rao lower bound. By extrapolating the CR bound, we argue that the imaging dose could be further lowered to 1 mGy, while still achieving submillimeter spatial resolution, by achievable instrumentation and beam delivery modifications. Conclusion: For proton radiography, high spatial and WET resolution can be achieved, with minimal additional dose to patient, by using magnetically scanned proton pencil beams and Micromegas detectors.« less

Multi-scale investigation of shrub encroachment in southern Africa

NASA Astrophysics Data System (ADS)

Aplin, Paul; Marston, Christopher; Wilkinson, David; Field, Richard; O'Regan, Hannah

2016-04-01

There is growing speculation that savannah environments throughout Africa have been subject to shrub encroachment in recent years, whereby grassland is lost to woody vegetation cover. Changes in the relative proportions of grassland and woodland are important in the context of conservation of savannah systems, with implications for faunal distributions, environmental management and tourism. Here, we focus on southern Kruger National Park, South Africa, and investigate whether or not shrub encroachment has occurred over the last decade and a half. We use a multi-scale approach, examining the complementarity of medium (e.g. Landsat TM and OLI) and fine (e.g. QuickBird and WorldView-2) spatial resolution satellite sensor imagery, supported by intensive field survey in 2002 and 2014. We employ semi-automated land cover classification, involving a hybrid unsupervised clustering approach with manual class grouping and checking, followed by change detection post-classification comparison analysis. The results show that shrub encroachment is indeed occurring, a finding evidenced through three fine resolution replicate images plus medium resolution imagery. The results also demonstrate the complementarity of medium and fine resolution imagery, though some thematic information must be sacrificed to maintain high medium resolution classification accuracy. Finally, the findings have broader implications for issues such as vegetation seasonality, spatial transferability and management practices.

Fusion of UAV photogrammetry and digital optical granulometry for detection of structural changes in floodplains

NASA Astrophysics Data System (ADS)

Langhammer, Jakub; Lendzioch, Theodora; Mirijovsky, Jakub

2016-04-01

Granulometric analysis represents a traditional, important and for the description of sedimentary material substantial method with various applications in sedimentology, hydrology and geomorphology. However, the conventional granulometric field survey methods are time consuming, laborious, costly and are invasive to the surface being sampled, which can be limiting factor for their applicability in protected areas.. The optical granulometry has recently emerged as an image analysis technique, enabling non-invasive survey, employing semi-automated identification of clasts from calibrated digital imagery, taken on site by conventional high resolution digital camera and calibrated frame. The image processing allows detection and measurement of mixed size natural grains, their sorting and quantitative analysis using standard granulometric approaches. Despite known limitations, the technique today presents reliable tool, significantly easing and speeding the field survey in fluvial geomorphology. However, the nature of such survey has still limitations in spatial coverage of the sites and applicability in research at multitemporal scale. In our study, we are presenting novel approach, based on fusion of two image analysis techniques - optical granulometry and UAV-based photogrammetry, allowing to bridge the gap between the needs of high resolution structural information for granulometric analysis and spatially accurate and data coverage. We have developed and tested a workflow that, using UAV imaging platform enabling to deliver seamless, high resolution and spatially accurate imagery of the study site from which can be derived the granulometric properties of the sedimentary material. We have set up a workflow modeling chain, providing (i) the optimum flight parameters for UAV imagery to balance the two key divergent requirements - imagery resolution and seamless spatial coverage, (ii) the workflow for the processing of UAV acquired imagery by means of the optical granulometry and (iii) the workflow for analysis of spatial distribution and temporal changes of granulometric properties across the point bar. The proposed technique was tested on a case study of an active point bar of mid-latitude mountain stream at Sumava mountains, Czech Republic, exposed to repeated flooding. The UAV photogrammetry was used to acquire very high resolution imagery to build high-precision digital terrain models and orthoimage. The orthoimage was then analyzed using the digital optical granulometric tool BaseGrain. This approach allowed us (i) to analyze the spatial distribution of the grain size in a seamless transects over an active point bar and (ii) to assess the multitemporal changes of granulometric properties of the point bar material resulting from flooding. The tested framework prove the applicability of the proposed method for granulometric analysis with accuracy comparable with field optical granulometry. The seamless nature of the data enables to study spatial distribution of granulometric properties across the study sites as well as the analysis of multitemporal changes, resulting from repeated imaging.

Grid-based mapping: A method for rapidly determining the spatial distributions of small features over very large areas

NASA Astrophysics Data System (ADS)

Ramsdale, Jason D.; Balme, Matthew R.; Conway, Susan J.; Gallagher, Colman; van Gasselt, Stephan A.; Hauber, Ernst; Orgel, Csilla; Séjourné, Antoine; Skinner, James A.; Costard, Francois; Johnsson, Andreas; Losiak, Anna; Reiss, Dennis; Swirad, Zuzanna M.; Kereszturi, Akos; Smith, Isaac B.; Platz, Thomas

2017-06-01

The increased volume, spatial resolution, and areal coverage of high-resolution images of Mars over the past 15 years have led to an increased quantity and variety of small-scale landform identifications. Though many such landforms are too small to represent individually on regional-scale maps, determining their presence or absence across large areas helps form the observational basis for developing hypotheses on the geological nature and environmental history of a study area. The combination of improved spatial resolution and near-continuous coverage significantly increases the time required to analyse the data. This becomes problematic when attempting regional or global-scale studies of metre and decametre-scale landforms. Here, we describe an approach for mapping small features (from decimetre to kilometre scale) across large areas, formulated for a project to study the northern plains of Mars, and provide context on how this method was developed and how it can be implemented. Rather than ;mapping; with points and polygons, grid-based mapping uses a ;tick box; approach to efficiently record the locations of specific landforms (we use an example suite of glacial landforms; including viscous flow features, the latitude dependant mantle and polygonised ground). A grid of squares (e.g. 20 km by 20 km) is created over the mapping area. Then the basemap data are systematically examined, grid-square by grid-square at full resolution, in order to identify the landforms while recording the presence or absence of selected landforms in each grid-square to determine spatial distributions. The result is a series of grids recording the distribution of all the mapped landforms across the study area. In some ways, these are equivalent to raster images, as they show a continuous distribution-field of the various landforms across a defined (rectangular, in most cases) area. When overlain on context maps, these form a coarse, digital landform map. We find that grid-based mapping provides an efficient solution to the problems of mapping small landforms over large areas, by providing a consistent and standardised approach to spatial data collection. The simplicity of the grid-based mapping approach makes it extremely scalable and workable for group efforts, requiring minimal user experience and producing consistent and repeatable results. The discrete nature of the datasets, simplicity of approach, and divisibility of tasks, open up the possibility for citizen science in which crowdsourcing large grid-based mapping areas could be applied.

Localization-based super-resolution imaging of cellular structures.

PubMed

Kanchanawong, Pakorn; Waterman, Clare M

2013-01-01

Fluorescence microscopy allows direct visualization of fluorescently tagged proteins within cells. However, the spatial resolution of conventional fluorescence microscopes is limited by diffraction to ~250 nm, prompting the development of super-resolution microscopy which offers resolution approaching the scale of single proteins, i.e., ~20 nm. Here, we describe protocols for single molecule localization-based super-resolution imaging, using focal adhesion proteins as an example and employing either photoswitchable fluorophores or photoactivatable fluorescent proteins. These protocols should also be easily adaptable to imaging a broad array of macromolecular assemblies in cells whose components can be fluorescently tagged and assemble into high density structures.

Comparative analysis of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), and Hyperspectral Thermal Emission Spectrometer (HyTES) longwave infrared (LWIR) hyperspectral data for geologic mapping

NASA Astrophysics Data System (ADS)

Kruse, Fred A.

2015-05-01

Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and spatially coincident Hyperspectral Thermal Emission Spectrometer (HyTES) data were used to map geology and alteration for a site in northern Death Valley, California and Nevada, USA. AVIRIS, with 224 bands at 10 nm spectral resolution over the range 0.4 - 2.5 μm at 3-meter spatial resolution were converted to reflectance using an atmospheric model. HyTES data with 256 bands at approximately 17 nm spectral resolution covering the 8 - 12 μm range at 4-meter spatial resolution were converted to emissivity using a longwave infrared (LWIR) radiative transfer atmospheric compensation model and a normalized temperature-emissivity separation approach. Key spectral endmembers were separately extracted for each wavelength region and identified, and the predominant material at each pixel was mapped for each range using Mixture-Tuned-Matched Filtering (MTMF), a partial unmixing approach. AVIRIS mapped iron oxides, clays, mica, and silicification (hydrothermal alteration); and the difference between calcite and dolomite. HyTES separated and mapped several igneous phases (not possible using AVIRIS), silicification, and validated separation of calcite from dolomite. Comparison of the material maps from the different modes, however, reveals complex overlap, indicating that multiple materials/processes exist in many areas. Combined and integrated analyses were performed to compare individual results and more completely characterize occurrences of multiple materials. Three approaches were used 1) integrated full-range analysis, 2) combined multimode classification, and 3) directed combined analysis in geologic context. Results illustrate that together, these two datasets provide an improved picture of the distribution of geologic units and subsequent alteration.

Georectification and snow classification of webcam images: potential for complementing satellite-derrived snow maps over Switzerland

NASA Astrophysics Data System (ADS)

Dizerens, Céline; Hüsler, Fabia; Wunderle, Stefan

2016-04-01

The spatial and temporal variability of snow cover has a significant impact on climate and environment and is of great socio-economic importance for the European Alps. Satellite remote sensing data is widely used to study snow cover variability and can provide spatially comprehensive information on snow cover extent. However, cloud cover strongly impedes the surface view and hence limits the number of useful snow observations. Outdoor webcam images not only offer unique potential for complementing satellite-derived snow retrieval under cloudy conditions but could also serve as a reference for improved validation of satellite-based approaches. Thousands of webcams are currently connected to the Internet and deliver freely available images with high temporal and spatial resolutions. To exploit the untapped potential of these webcams, a semi-automatic procedure was developed to generate snow cover maps based on webcam images. We used daily webcam images of the Swiss alpine region to apply, improve, and extend existing approaches dealing with the positioning of photographs within a terrain model, appropriate georectification, and the automatic snow classification of such photographs. In this presentation, we provide an overview of the implemented procedure and demonstrate how our registration approach automatically resolves the orientation of a webcam by using a high-resolution digital elevation model and the webcam's position. This allows snow-classified pixels of webcam images to be related to their real-world coordinates. We present several examples of resulting snow cover maps, which have the same resolution as the digital elevation model and indicate whether each grid cell is snow-covered, snow-free, or not visible from webcams' positions. The procedure is expected to work under almost any weather condition and demonstrates the feasibility of using webcams for the retrieval of high-resolution snow cover information.

High-Resolution Soil Moisture Retrieval using SMAP-L Band Radiometer and RISAT-C band Radar Data for the Indian Subcontinent

NASA Astrophysics Data System (ADS)

Singh, G.; Das, N. N.; Panda, R. K.; Mohanty, B.; Entekhabi, D.; Bhattacharya, B. K.

2016-12-01

Soil moisture status at high resolution (1-10 km) is vital for hydrological, agricultural and hydro-metrological applications. The NASA Soil Moisture Active Passive (SMAP) mission had potential to provide reliable soil moisture estimate at finer spatial resolutions (3 km and 9 km) at the global extent, but suffered a malfunction of its radar, consequently making the SMAP mission observations only from radiometer that are of coarse spatial resolution. At present, the availability of high-resolution soil moisture product is limited, especially in developing countries like India, which greatly depends on agriculture for sustaining a huge population. Therefore, an attempt has been made in the reported study to combine the C-band synthetic aperture radar (SAR) data from Radar Imaging Satellite (RISAT) of the Indian Space Research Organization (ISRO) with the SMAP mission L-band radiometer data to obtain high-resolution (1 km and 3 km) soil moisture estimates. In this study, a downscaling approach (Active-Passive Algorithm) implemented for the SMAP mission was used to disaggregate the SMAP radiometer brightness temperature (Tb) using the fine resolution SAR backscatter (σ0) from RISAT. The downscaled high-resolution Tb was then subjected to tau-omega model in conjunction with high-resolution ancillary data to retrieve soil moisture at 1 and 3 km scale. The retrieved high-resolution soil moisture estimates were then validated with ground based soil moisture measurement under different hydro-climatic regions of India. Initial results show tremendous potential and reasonable accuracy for the retrieved soil moisture at 1 km and 3 km. It is expected that ISRO will implement this approach to produce high-resolution soil moisture estimates for the Indian subcontinent.

Drone based estimation of actual evapotranspiration over different forest types

NASA Astrophysics Data System (ADS)

Marzahn, Philip; Gampe, David; Castro, Saulo; Vega-Araya, Mauricio; Sanchez-Azofeifa, Arturo; Ludwig, Ralf

2017-04-01

Actual evapotranspiration (Eta) plays an important role in surface-atmosphere interactions. Traditionally, Eta is measured by means of lysimeters, eddy-covariance systems or fiber optics, providing estimates which are spatially restricted to a footprint from a few square meters up to several hectares . In the past, several methods have been developed to derive Eta by means of multi-spectral remote sensing data using thermal and VIS/NIR satellite imagery of the land surface. As such approaches do have their justification on coarser scales, they do not provide Eta information on the fine resolution plant level over large areas which is mandatory for the detection of water stress or tree mortality. In this study, we present a comparison of a drone based assessment of Eta with eddy-covariance measurements over two different forest types - a deciduous forest in Alberta, Canada and a tropical dry forest in Costa Rica. Drone based estimates of Eta were calculated applying the Triangle-Method proposed by Jiang and Islam (1999). The Triangle-Method estimates actual evapotranspiration (Eta) by means of the Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) provided by two camera systems (MicaSense RedEdge, FLIR TAU2 640) flown simultaneously on an octocopter. . Results indicate a high transferability of the original approach from Jiang and Islam (1999) developed for coarse to medium resolution satellite imagery tothe high resolution drone data, leading to a deviation in Eta estimates of 10% compared to the eddy-covariance measurements. In addition, the spatial footprint of the eddy-covariance measurement can be detected with this approach, by showing the spatial heterogeneities of Eta due to the spatial distribution of different trees and understory vegetation.

Temporal rainfall disaggregation using a multiplicative cascade model for spatial application in urban hydrology

NASA Astrophysics Data System (ADS)

Müller, H.; Haberlandt, U.

2018-01-01

Rainfall time series of high temporal resolution and spatial density are crucial for urban hydrology. The multiplicative random cascade model can be used for temporal rainfall disaggregation of daily data to generate such time series. Here, the uniform splitting approach with a branching number of 3 in the first disaggregation step is applied. To achieve a final resolution of 5 min, subsequent steps after disaggregation are necessary. Three modifications at different disaggregation levels are tested in this investigation (uniform splitting at Δt = 15 min, linear interpolation at Δt = 7.5 min and Δt = 3.75 min). Results are compared both with observations and an often used approach, based on the assumption that a time steps with Δt = 5.625 min, as resulting if a branching number of 2 is applied throughout, can be replaced with Δt = 5 min (called the 1280 min approach). Spatial consistence is implemented in the disaggregated time series using a resampling algorithm. In total, 24 recording stations in Lower Saxony, Northern Germany with a 5 min resolution have been used for the validation of the disaggregation procedure. The urban-hydrological suitability is tested with an artificial combined sewer system of about 170 hectares. The results show that all three variations outperform the 1280 min approach regarding reproduction of wet spell duration, average intensity, fraction of dry intervals and lag-1 autocorrelation. Extreme values with durations of 5 min are also better represented. For durations of 1 h, all approaches show only slight deviations from the observed extremes. The applied resampling algorithm is capable to achieve sufficient spatial consistence. The effects on the urban hydrological simulations are significant. Without spatial consistence, flood volumes of manholes and combined sewer overflow are strongly underestimated. After resampling, results using disaggregated time series as input are in the range of those using observed time series. Best overall performance regarding rainfall statistics are obtained by the method in which the disaggregation process ends at time steps with 7.5 min duration, deriving the 5 min time steps by linear interpolation. With subsequent resampling this method leads to a good representation of manhole flooding and combined sewer overflow volume in terms of hydrological simulations and outperforms the 1280 min approach.

An Integrated Photogrammetric and Photoclinometric Approach for Pixel-Resolution 3d Modelling of Lunar Surface

NASA Astrophysics Data System (ADS)

Liu, W. C.; Wu, B.

2018-04-01

High-resolution 3D modelling of lunar surface is important for lunar scientific research and exploration missions. Photogrammetry is known for 3D mapping and modelling from a pair of stereo images based on dense image matching. However dense matching may fail in poorly textured areas and in situations when the image pair has large illumination differences. As a result, the actual achievable spatial resolution of the 3D model from photogrammetry is limited by the performance of dense image matching. On the other hand, photoclinometry (i.e., shape from shading) is characterised by its ability to recover pixel-wise surface shapes based on image intensity and imaging conditions such as illumination and viewing directions. More robust shape reconstruction through photoclinometry can be achieved by incorporating images acquired under different illumination conditions (i.e., photometric stereo). Introducing photoclinometry into photogrammetric processing can therefore effectively increase the achievable resolution of the mapping result while maintaining its overall accuracy. This research presents an integrated photogrammetric and photoclinometric approach for pixel-resolution 3D modelling of the lunar surface. First, photoclinometry is interacted with stereo image matching to create robust and spatially well distributed dense conjugate points. Then, based on the 3D point cloud derived from photogrammetric processing of the dense conjugate points, photoclinometry is further introduced to derive the 3D positions of the unmatched points and to refine the final point cloud. The approach is able to produce one 3D point for each image pixel within the overlapping area of the stereo pair so that to obtain pixel-resolution 3D models. Experiments using the Lunar Reconnaissance Orbiter Camera - Narrow Angle Camera (LROC NAC) images show the superior performances of the approach compared with traditional photogrammetric technique. The results and findings from this research contribute to optimal exploitation of image information for high-resolution 3D modelling of the lunar surface, which is of significance for the advancement of lunar and planetary mapping.

Determination of forest fuels characteristics in mortality-affected Pinus forests using integrated hyperspectral and ALS data

NASA Astrophysics Data System (ADS)

Romero Ramirez, Francisco J.; Navarro-Cerrillo, Rafael Mª.; Varo-Martínez, Mª. Ángeles; Quero, Jose Luis; Doerr, Stefan; Hernández-Clemente, Rocío

2018-06-01

Widespread tree mortality caused by forest decline in recent decades has raised concern among forest managers about how to assess forest fuels in these conditions. To investigate this question, we developed and tested an objective, consistent approach to the characterization of canopy fuel metrics - such as fuel load (FL), live fuel moisture content (LFMC), and live-dead ratio (LDR) - by integrating airborne laser scanning (ALS) and hyperspectral data to produce more-accurate estimates at the stand level. Regression models were developed for Pinus sylvestris and P. nigra stands representative of pine plantations in southern Spain, using field data acquired for different spatial fuel types and distributions as well as high resolution airborne hyperspectral data (AHS) and ALS datasets. Strong relationships were found between ALS and FL using a density of 2 points m-2 (R2 = 0.64) and between LFMC and Temperature/NDVI index at a spatial resolution of 5 m (R2 = 0.91). The red edge normalized index provided the highest separability (Jeffries-Matusita distance = 1.83) between types of LDR. The plot-aggregate ALS and AHS metrics performed better at spatial resolutions of 5 m and 2 points m-2 than at other scales. Cartography of the estimations of FL, LFMC, and LDR made using the empirical models from the ALS and AHS data showed a mean FL value of 65.87 Mg ha-1, an average LFMC content of 57.51%, and 30.75% of the surface classified as dead fuel (≥60% defoliation). The results suggest that our remote sensing approach could improve the estimation of canopy fuels characteristics at higher spatial resolutions as well as estimations of fuel cartography, to assist the planning and management of fuel reduction treatments.

A High-Resolution Spatially Explicit Monte-Carlo Simulation Approach to Commercial and Residential Electricity and Water Demand Modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Morton, April M; McManamay, Ryan A; Nagle, Nicholas N

Abstract As urban areas continue to grow and evolve in a world of increasing environmental awareness, the need for high resolution spatially explicit estimates for energy and water demand has become increasingly important. Though current modeling efforts mark significant progress in the effort to better understand the spatial distribution of energy and water consumption, many are provided at a course spatial resolution or rely on techniques which depend on detailed region-specific data sources that are not publicly available for many parts of the U.S. Furthermore, many existing methods do not account for errors in input data sources and may thereforemore » not accurately reflect inherent uncertainties in model outputs. We propose an alternative and more flexible Monte-Carlo simulation approach to high-resolution residential and commercial electricity and water consumption modeling that relies primarily on publicly available data sources. The method s flexible data requirement and statistical framework ensure that the model is both applicable to a wide range of regions and reflective of uncertainties in model results. Key words: Energy Modeling, Water Modeling, Monte-Carlo Simulation, Uncertainty Quantification Acknowledgment This manuscript has been authored by employees of UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy. Accordingly, the United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.« less

A deterministic model of electron transport for electron probe microanalysis

NASA Astrophysics Data System (ADS)

Bünger, J.; Richter, S.; Torrilhon, M.

2018-01-01

Within the last decades significant improvements in the spatial resolution of electron probe microanalysis (EPMA) were obtained by instrumental enhancements. In contrast, the quantification procedures essentially remained unchanged. As the classical procedures assume either homogeneity or a multi-layered structure of the material, they limit the spatial resolution of EPMA. The possibilities of improving the spatial resolution through more sophisticated quantification procedures are therefore almost untouched. We investigate a new analytical model (M 1-model) for the quantification procedure based on fast and accurate modelling of electron-X-ray-matter interactions in complex materials using a deterministic approach to solve the electron transport equations. We outline the derivation of the model from the Boltzmann equation for electron transport using the method of moments with a minimum entropy closure and present first numerical results for three different test cases (homogeneous, thin film and interface). Taking Monte Carlo as a reference, the results for the three test cases show that the M 1-model is able to reproduce the electron dynamics in EPMA applications very well. Compared to classical analytical models like XPP and PAP, the M 1-model is more accurate and far more flexible, which indicates the potential of deterministic models of electron transport to further increase the spatial resolution of EPMA.

Atomic-Scale Nuclear Spin Imaging Using Quantum-Assisted Sensors in Diamond

NASA Astrophysics Data System (ADS)

Ajoy, A.; Bissbort, U.; Lukin, M. D.; Walsworth, R. L.; Cappellaro, P.

2015-01-01

Nuclear spin imaging at the atomic level is essential for the understanding of fundamental biological phenomena and for applications such as drug discovery. The advent of novel nanoscale sensors promises to achieve the long-standing goal of single-protein, high spatial-resolution structure determination under ambient conditions. In particular, quantum sensors based on the spin-dependent photoluminescence of nitrogen-vacancy (NV) centers in diamond have recently been used to detect nanoscale ensembles of external nuclear spins. While NV sensitivity is approaching single-spin levels, extracting relevant information from a very complex structure is a further challenge since it requires not only the ability to sense the magnetic field of an isolated nuclear spin but also to achieve atomic-scale spatial resolution. Here, we propose a method that, by exploiting the coupling of the NV center to an intrinsic quantum memory associated with the nitrogen nuclear spin, can reach a tenfold improvement in spatial resolution, down to atomic scales. The spatial resolution enhancement is achieved through coherent control of the sensor spin, which creates a dynamic frequency filter selecting only a few nuclear spins at a time. We propose and analyze a protocol that would allow not only sensing individual spins in a complex biomolecule, but also unraveling couplings among them, thus elucidating local characteristics of the molecule structure.

Time of flight dependent linearity in diffuse imaging: how effective is it to evaluate the spatial resolution by measuring the edge response function?

PubMed

Ortiz-Rascón, E; Bruce, N C; Rodríguez-Rosales, A A; Garduño-Mejía, J

2016-03-01

We describe the behavior of linearity in diffuse imaging by evaluating the differences between time-resolved images produced by photons arriving at the detector at different times. Two approaches are considered: Monte Carlo simulations and experimental results. The images of two complete opaque bars embedded in a transparent or in a turbid medium with a slab geometry are analyzed; the optical properties of the turbid medium sample are close to those of breast tissue. A simple linearity test was designed involving a direct comparison between the intensity profile produced by two bars scanned at the same time and the intensity profile obtained by adding two profiles of each bar scanned one at a time. It is shown that the linearity improves substantially when short time of flight photons are used in the imaging process, but even then the nonlinear behavior prevails. As the edge response function (ERF) has been used widely for testing the spatial resolution in imaging systems, the main implication of a time dependent linearity is the weakness of the linearity assumption when evaluating the spatial resolution through the ERF in diffuse imaging systems, and the need to evaluate the spatial resolution by other methods.

Magneto-optical imaging of thin magnetic films using spins in diamond

NASA Astrophysics Data System (ADS)

Simpson, David A.; Tetienne, Jean-Philippe; McCoey, Julia M.; Ganesan, Kumaravelu; Hall, Liam T.; Petrou, Steven; Scholten, Robert E.; Hollenberg, Lloyd C. L.

2016-03-01

Imaging the fields of magnetic materials provides crucial insight into the physical and chemical processes surrounding magnetism, and has been a key ingredient in the spectacular development of magnetic data storage. Existing approaches using the magneto-optic Kerr effect, x-ray and electron microscopy have limitations that constrain further development, and there is increasing demand for imaging and characterisation of magnetic phenomena in real time with high spatial resolution. Here we show how the magneto-optical response of an array of negatively-charged nitrogen-vacancy spins in diamond can be used to image and map the sub-micron stray magnetic field patterns from thin ferromagnetic films. Using optically detected magnetic resonance, we demonstrate wide-field magnetic imaging over 100 × 100 μm2 with sub-micron spatial resolution at video frame rates, under ambient conditions. We demonstrate an all-optical spin relaxation contrast imaging approach which can image magnetic structures in the absence of an applied microwave field. Straightforward extensions promise imaging with sub-μT sensitivity and sub-optical spatial and millisecond temporal resolution. This work establishes practical diamond-based wide-field microscopy for rapid high-sensitivity characterisation and imaging of magnetic samples, with the capability for investigating magnetic phenomena such as domain wall and skyrmion dynamics and the spin Hall effect in metals.

Photo-induced spatial modulation of THz waves: opportunities and limitations.

PubMed

Kannegulla, Akash; Shams, Md Itrat Bin; Liu, Lei; Cheng, Li-Jing

2015-12-14

Programmable conductive patterns created by photoexcitation of semiconductor substrates using digital light processing (DLP) provides a versatile approach for spatial and temporal modulation of THz waves. The reconfigurable nature of the technology has great potential in implementing several promising THz applications, such as THz beam steering, THz imaging or THz remote sensing, in a simple, cost-effective manner. In this paper, we provide physical insight about how the semiconducting materials, substrate dimension, optical illumination wavelength and illumination size impact the performance of THz modulation, including modulation depth, modulation speed and spatial resolution. The analysis establishes design guidelines for the development of photo-induced THz modulation technology. Evolved from the theoretical analysis, a new mesa array technology composed by a matrix of sub-THz wavelength structures is introduced to maximize both spatial resolution and modulation depth for THz modulation with low-power photoexcitation by prohibiting the lateral diffusion of photogenerated carriers.

Effects of spatial resolution ratio in image fusion

USGS Publications Warehouse

Ling, Y.; Ehlers, M.; Usery, E.L.; Madden, M.

2008-01-01

In image fusion, the spatial resolution ratio can be defined as the ratio between the spatial resolution of the high-resolution panchromatic image and that of the low-resolution multispectral image. This paper attempts to assess the effects of the spatial resolution ratio of the input images on the quality of the fused image. Experimental results indicate that a spatial resolution ratio of 1:10 or higher is desired for optimal multisensor image fusion provided the input panchromatic image is not downsampled to a coarser resolution. Due to the synthetic pixels generated from resampling, the quality of the fused image decreases as the spatial resolution ratio decreases (e.g. from 1:10 to 1:30). However, even with a spatial resolution ratio as small as 1:30, the quality of the fused image is still better than the original multispectral image alone for feature interpretation. In cases where the spatial resolution ratio is too small (e.g. 1:30), to obtain better spectral integrity of the fused image, one may downsample the input high-resolution panchromatic image to a slightly lower resolution before fusing it with the multispectral image.

Comparing Different Approaches for Mapping Urban Vegetation Cover from Landsat ETM+ Data: A Case Study on Brussels

PubMed Central

Van de Voorde, Tim; Vlaeminck, Jeroen; Canters, Frank

2008-01-01

Urban growth and its related environmental problems call for sustainable urban management policies to safeguard the quality of urban environments. Vegetation plays an important part in this as it provides ecological, social, health and economic benefits to a city's inhabitants. Remotely sensed data are of great value to monitor urban green and despite the clear advantages of contemporary high resolution images, the benefits of medium resolution data should not be discarded. The objective of this research was to estimate fractional vegetation cover from a Landsat ETM+ image with sub-pixel classification, and to compare accuracies obtained with multiple stepwise regression analysis, linear spectral unmixing and multi-layer perceptrons (MLP) at the level of meaningful urban spatial entities. Despite the small, but nevertheless statistically significant differences at pixel level between the alternative approaches, the spatial pattern of vegetation cover and estimation errors is clearly distinctive at neighbourhood level. At this spatially aggregated level, a simple regression model appears to attain sufficient accuracy. For mapping at a spatially more detailed level, the MLP seems to be the most appropriate choice. Brightness normalisation only appeared to affect the linear models, especially the linear spectral unmixing. PMID:27879914

Spatially and momentum resolved energy electron loss spectra from an ultra-thin PrNiO{sub 3} layer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kinyanjui, M. K., E-mail: michael.kinyanjui@uni-ulm.de; Kaiser, U.; Benner, G.

2015-05-18

We present an experimental approach which allows for the acquisition of spectra from ultra-thin films at high spatial, momentum, and energy resolutions. Spatially and momentum (q) resolved electron energy loss spectra have been obtained from a 12 nm ultra-thin PrNiO{sub 3} layer using a nano-beam electron diffraction based approach which enabled the acquisition of momentum resolved spectra from individual, differently oriented nano-domains and at different positions of the PrNiO{sub 3} thin layer. The spatial and wavelength dependence of the spectral excitations are obtained and characterized after the analysis of the experimental spectra using calculated dielectric and energy loss functions. The presentedmore » approach makes a contribution towards obtaining momentum-resolved spectra from nanostructures, thin film, heterostructures, surfaces, and interfaces.« less

Los Angeles megacity: a high-resolution land–atmosphere modelling system for urban CO 2 emissions

DOE PAGES

Feng, Sha; Lauvaux, Thomas; Newman, Sally; ...

2016-07-22

Megacities are major sources of anthropogenic fossil fuel CO 2 (FFCO 2) emissions. The spatial extents of these large urban systems cover areas of 10 000 km 2 or more with complex topography and changing landscapes. We present a high-resolution land–atmosphere modelling system for urban CO 2 emissions over the Los Angeles (LA) megacity area. The Weather Research and Forecasting (WRF)-Chem model was coupled to a very high-resolution FFCO 2 emission product, Hestia-LA, to simulate atmospheric CO 2 concentrations across the LA megacity at spatial resolutions as fine as ~1 km. We evaluated multiple WRF configurations, selecting one that minimizedmore » errors in wind speed, wind direction, and boundary layer height as evaluated by its performance against meteorological data collected during the CalNex-LA campaign (May–June 2010). Our results show no significant difference between moderate-resolution (4 km) and high-resolution (1.3 km) simulations when evaluated against surface meteorological data, but the high-resolution configurations better resolved planetary boundary layer heights and vertical gradients in the horizontal mean winds. We coupled our WRF configuration with the Vulcan 2.2 (10 km resolution) and Hestia-LA (1.3 km resolution) fossil fuel CO 2 emission products to evaluate the impact of the spatial resolution of the CO 2 emission products and the meteorological transport model on the representation of spatiotemporal variability in simulated atmospheric CO 2 concentrations. We find that high spatial resolution in the fossil fuel CO 2 emissions is more important than in the atmospheric model to capture CO 2 concentration variability across the LA megacity. Finally, we present a novel approach that employs simultaneous correlations of the simulated atmospheric CO 2 fields to qualitatively evaluate the greenhouse gas measurement network over the LA megacity. Spatial correlations in the atmospheric CO 2 fields reflect the coverage of individual measurement sites when a statistically significant number of sites observe emissions from a specific source or location. We conclude that elevated atmospheric CO 2 concentrations over the LA megacity are composed of multiple fine-scale plumes rather than a single homogenous urban dome. Furthermore, we conclude that FFCO 2 emissions monitoring in the LA megacity requires FFCO 2 emissions modelling with ~1 km resolution because coarser-resolution emissions modelling tends to overestimate the observational constraints on the emissions estimates.« less

Los Angeles megacity: a high-resolution land–atmosphere modelling system for urban CO 2 emissions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Feng, Sha; Lauvaux, Thomas; Newman, Sally

Megacities are major sources of anthropogenic fossil fuel CO 2 (FFCO 2) emissions. The spatial extents of these large urban systems cover areas of 10 000 km 2 or more with complex topography and changing landscapes. We present a high-resolution land–atmosphere modelling system for urban CO 2 emissions over the Los Angeles (LA) megacity area. The Weather Research and Forecasting (WRF)-Chem model was coupled to a very high-resolution FFCO 2 emission product, Hestia-LA, to simulate atmospheric CO 2 concentrations across the LA megacity at spatial resolutions as fine as ~1 km. We evaluated multiple WRF configurations, selecting one that minimizedmore » errors in wind speed, wind direction, and boundary layer height as evaluated by its performance against meteorological data collected during the CalNex-LA campaign (May–June 2010). Our results show no significant difference between moderate-resolution (4 km) and high-resolution (1.3 km) simulations when evaluated against surface meteorological data, but the high-resolution configurations better resolved planetary boundary layer heights and vertical gradients in the horizontal mean winds. We coupled our WRF configuration with the Vulcan 2.2 (10 km resolution) and Hestia-LA (1.3 km resolution) fossil fuel CO 2 emission products to evaluate the impact of the spatial resolution of the CO 2 emission products and the meteorological transport model on the representation of spatiotemporal variability in simulated atmospheric CO 2 concentrations. We find that high spatial resolution in the fossil fuel CO 2 emissions is more important than in the atmospheric model to capture CO 2 concentration variability across the LA megacity. Finally, we present a novel approach that employs simultaneous correlations of the simulated atmospheric CO 2 fields to qualitatively evaluate the greenhouse gas measurement network over the LA megacity. Spatial correlations in the atmospheric CO 2 fields reflect the coverage of individual measurement sites when a statistically significant number of sites observe emissions from a specific source or location. We conclude that elevated atmospheric CO 2 concentrations over the LA megacity are composed of multiple fine-scale plumes rather than a single homogenous urban dome. Furthermore, we conclude that FFCO 2 emissions monitoring in the LA megacity requires FFCO 2 emissions modelling with ~1 km resolution because coarser-resolution emissions modelling tends to overestimate the observational constraints on the emissions estimates.« less

Technical Note: Synchrotron-based high-energy x-ray phase sensitive microtomography for biomedical research

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Huiqiang; Wu, Xizeng, E-mail: xwu@uabmc.edu, E-mail: tqxiao@sinap.ac.cn; Xiao, Tiqiao, E-mail: xwu@uabmc.edu, E-mail: tqxiao@sinap.ac.cn

Purpose: Propagation-based phase-contrast CT (PPCT) utilizes highly sensitive phase-contrast technology applied to x-ray microtomography. Performing phase retrieval on the acquired angular projections can enhance image contrast and enable quantitative imaging. In this work, the authors demonstrate the validity and advantages of a novel technique for high-resolution PPCT by using the generalized phase-attenuation duality (PAD) method of phase retrieval. Methods: A high-resolution angular projection data set of a fish head specimen was acquired with a monochromatic 60-keV x-ray beam. In one approach, the projection data were directly used for tomographic reconstruction. In two other approaches, the projection data were preprocessed bymore » phase retrieval based on either the linearized PAD method or the generalized PAD method. The reconstructed images from all three approaches were then compared in terms of tissue contrast-to-noise ratio and spatial resolution. Results: The authors’ experimental results demonstrated the validity of the PPCT technique based on the generalized PAD-based method. In addition, the results show that the authors’ technique is superior to the direct PPCT technique as well as the linearized PAD-based PPCT technique in terms of their relative capabilities for tissue discrimination and characterization. Conclusions: This novel PPCT technique demonstrates great potential for biomedical imaging, especially for applications that require high spatial resolution and limited radiation exposure.« less

Penalized Weighted Least-Squares Approach to Sinogram Noise Reduction and Image Reconstruction for Low-Dose X-Ray Computed Tomography

PubMed Central

Wang, Jing; Li, Tianfang; Lu, Hongbing; Liang, Zhengrong

2006-01-01

Reconstructing low-dose X-ray CT (computed tomography) images is a noise problem. This work investigated a penalized weighted least-squares (PWLS) approach to address this problem in two dimensions, where the WLS considers first- and second-order noise moments and the penalty models signal spatial correlations. Three different implementations were studied for the PWLS minimization. One utilizes a MRF (Markov random field) Gibbs functional to consider spatial correlations among nearby detector bins and projection views in sinogram space and minimizes the PWLS cost function by iterative Gauss-Seidel algorithm. Another employs Karhunen-Loève (KL) transform to de-correlate data signals among nearby views and minimizes the PWLS adaptively to each KL component by analytical calculation, where the spatial correlation among nearby bins is modeled by the same Gibbs functional. The third one models the spatial correlations among image pixels in image domain also by a MRF Gibbs functional and minimizes the PWLS by iterative successive over-relaxation algorithm. In these three implementations, a quadratic functional regularization was chosen for the MRF model. Phantom experiments showed a comparable performance of these three PWLS-based methods in terms of suppressing noise-induced streak artifacts and preserving resolution in the reconstructed images. Computer simulations concurred with the phantom experiments in terms of noise-resolution tradeoff and detectability in low contrast environment. The KL-PWLS implementation may have the advantage in terms of computation for high-resolution dynamic low-dose CT imaging. PMID:17024831

A generic method for improving the spatial interoperability of medical and ecological databases.

PubMed

Ghenassia, A; Beuscart, J B; Ficheur, G; Occelli, F; Babykina, E; Chazard, E; Genin, M

2017-10-03

The availability of big data in healthcare and the intensive development of data reuse and georeferencing have opened up perspectives for health spatial analysis. However, fine-scale spatial studies of ecological and medical databases are limited by the change of support problem and thus a lack of spatial unit interoperability. The use of spatial disaggregation methods to solve this problem introduces errors into the spatial estimations. Here, we present a generic, two-step method for merging medical and ecological databases that avoids the use of spatial disaggregation methods, while maximizing the spatial resolution. Firstly, a mapping table is created after one or more transition matrices have been defined. The latter link the spatial units of the original databases to the spatial units of the final database. Secondly, the mapping table is validated by (1) comparing the covariates contained in the two original databases, and (2) checking the spatial validity with a spatial continuity criterion and a spatial resolution index. We used our novel method to merge a medical database (the French national diagnosis-related group database, containing 5644 spatial units) with an ecological database (produced by the French National Institute of Statistics and Economic Studies, and containing with 36,594 spatial units). The mapping table yielded 5632 final spatial units. The mapping table's validity was evaluated by comparing the number of births in the medical database and the ecological databases in each final spatial unit. The median [interquartile range] relative difference was 2.3% [0; 5.7]. The spatial continuity criterion was low (2.4%), and the spatial resolution index was greater than for most French administrative areas. Our innovative approach improves interoperability between medical and ecological databases and facilitates fine-scale spatial analyses. We have shown that disaggregation models and large aggregation techniques are not necessarily the best ways to tackle the change of support problem.

Evaluation of Pan-Sharpening Methods for Automatic Shadow Detection in High Resolution Images of Urban Areas

NASA Astrophysics Data System (ADS)

de Azevedo, Samara C.; Singh, Ramesh P.; da Silva, Erivaldo A.

2017-04-01

Finer spatial resolution of areas with tall objects within urban environment causes intense shadows that lead to wrong information in urban mapping. Due to the shadows, automatic detection of objects (such as buildings, trees, structures, towers) and to estimate the surface coverage from high spatial resolution is difficult. Thus, automatic shadow detection is the first necessary preprocessing step to improve the outcome of many remote sensing applications, particularly for high spatial resolution images. Efforts have been made to explore spatial and spectral information to evaluate such shadows. In this paper, we have used morphological attribute filtering to extract contextual relations in an efficient multilevel approach for high resolution images. The attribute selected for the filtering was the area estimated from shadow spectral feature using the Normalized Saturation-Value Difference Index (NSVDI) derived from pan-sharpening images. In order to assess the quality of fusion products and the influence on shadow detection algorithm, we evaluated three pan-sharpening methods - Intensity-Hue-Saturation (IHS), Principal Components (PC) and Gran-Schmidt (GS) through the image quality measures: Correlation Coefficient (CC), Root Mean Square Error (RMSE), Relative Dimensionless Global Error in Synthesis (ERGAS) and Universal Image Quality Index (UIQI). Experimental results over Worldview II scene from São Paulo city (Brazil) show that GS method provides good correlation with original multispectral bands with no radiometric and contrast distortion. The automatic method using GS method for NSDVI generation clearly provide a clear distinction of shadows and non-shadows pixels with an overall accuracy more than 90%. The experimental results confirm the effectiveness of the proposed approach which could be used for further shadow removal and reliable for object recognition, land-cover mapping, 3D reconstruction, etc. especially in developing countries where land use and land cover are rapidly changing with tall objects within urban areas.

Simulating Future Changes in Spatio-temporal Precipitation by Identifying and Characterizing Individual Rainstorm Events

NASA Astrophysics Data System (ADS)

Chang, W.; Stein, M.; Wang, J.; Kotamarthi, V. R.; Moyer, E. J.

2015-12-01

A growing body of literature suggests that human-induced climate change may cause significant changes in precipitation patterns, which could in turn influence future flood levels and frequencies and water supply and management practices. Although climate models produce full three-dimensional simulations of precipitation, analyses of model precipitation have focused either on time-averaged distributions or on individual timeseries with no spatial information. We describe here a new approach based on identifying and characterizing individual rainstorms in either data or model output. Our approach enables us to readily characterize important spatio-temporal aspects of rainstorms including initiation location, intensity (mean and patterns), spatial extent, duration, and trajectory. We apply this technique to high-resolution precipitation over the continental U.S. both from radar-based observations (NCEP Stage IV QPE product, 1-hourly, 4 km spatial resolution) and from model runs with dynamical downscaling (WRF regional climate model, 3-hourly, 12 km spatial resolution). In the model studies we investigate the changes in storm characteristics under a business-as-usual warming scenario to 2100 (RCP 8.5). We find that in these model runs, rainstorm intensity increases as expected with rising temperatures (approximately 7%/K, following increased atmospheric moisture content), while total precipitation increases by a lesser amount (3%/K), consistent with other studies. We identify for the first time the necessary compensating mechanism: in these model runs, individual precipitation events become smaller. Other aspects are approximately unchanged in the warmer climate. Because these spatio-temporal changes in rainfall patterns would impact regional hydrology, it is important that they be accurately incorporated into any impacts assessment. For this purpose we have developed a methodology for producing scenarios of future precipitation that combine observational data and model-projected changes. We statistically describe the future changes in rainstorm characteristics suggested by the WRF model and apply those changes to observational data. The resulting high spatial and temporal resolution scenarios have immediate applications for impacts assessment and adaptation studies.

Fresnel zone plate stacking in the intermediate field for high efficiency focusing in the hard X-ray regime

DOE PAGES

Gleber, Sophie -Charlotte; Wojcik, Michael; Liu, Jie; ...

2014-11-05

Focusing efficiency of Fresnel zone plates (FZPs) for X-rays depends on zone height, while the achievable spatial resolution depends on the width of the finest zones. FZPs with optimal efficiency and sub-100-nm spatial resolution require high aspect ratio structures which are difficult to fabricate with current technology especially for the hard X-ray regime. A possible solution is to stack several zone plates. To increase the number of FZPs within one stack, we first demonstrate intermediate-field stacking and apply this method by stacks of up to five FZPs with adjusted diameters. Approaching the respective optimum zone height, we maximized efficiencies formore » high resolution focusing at three different energies, 10, 11.8, and 25 keV.« less

Generating Vegetation Leaf Area Index Earth System Data Record from Multiple Sensors. Part 1; Theory

NASA Technical Reports Server (NTRS)

Ganguly, Sangram; Schull, Mitchell A.; Samanta, Arindam; Shabanov, Nikolay V.; Milesi, Cristina; Nemani, Ramakrishna R.; Knyazikhin, Yuri; Myneni, Ranga B.

2008-01-01

The generation of multi-decade long Earth System Data Records (ESDRs) of Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) from remote sensing measurements of multiple sensors is key to monitoring long-term changes in vegetation due to natural and anthropogenic influences. Challenges in developing such ESDRs include problems in remote sensing science (modeling of variability in global vegetation, scaling, atmospheric correction) and sensor hardware (differences in spatial resolution, spectral bands, calibration, and information content). In this paper, we develop a physically based approach for deriving LAI and FPAR products from the Advanced Very High Resolution Radiometer (AVHRR) data that are of comparable quality to the Moderate resolution Imaging Spectroradiometer (MODIS) LAI and FPAR products, thus realizing the objective of producing a long (multi-decadal) time series of these products. The approach is based on the radiative transfer theory of canopy spectral invariants which facilitates parameterization of the canopy spectral bidirectional reflectance factor (BRF). The methodology permits decoupling of the structural and radiometric components and obeys the energy conservation law. The approach is applicable to any optical sensor, however, it requires selection of sensor-specific values of configurable parameters, namely, the single scattering albedo and data uncertainty. According to the theory of spectral invariants, the single scattering albedo is a function of the spatial scale, and thus, accounts for the variation in BRF with sensor spatial resolution. Likewise, the single scattering albedo accounts for the variation in spectral BRF with sensor bandwidths. The second adjustable parameter is data uncertainty, which accounts for varying information content of the remote sensing measurements, i.e., Normalized Difference Vegetation Index (NDVI, low information content), vs. spectral BRF (higher information content). Implementation of this approach indicates good consistency in LAI values retrieved from NDVI (AVHRRmode) and spectral BRF (MODIS-mode). Specific details of the implementation and evaluation of the derived products are detailed in the second part of this two-paper series.

A comparison of multi-spectral, multi-angular, and multi-temporal remote sensing datasets for fractional shrub canopy mapping in Arctic Alaska

USGS Publications Warehouse

Selkowitz, D.J.

2010-01-01

Shrub cover appears to be increasing across many areas of the Arctic tundra biome, and increasing shrub cover in the Arctic has the potential to significantly impact global carbon budgets and the global climate system. For most of the Arctic, however, there is no existing baseline inventory of shrub canopy cover, as existing maps of Arctic vegetation provide little information about the density of shrub cover at a moderate spatial resolution across the region. Remotely-sensed fractional shrub canopy maps can provide this necessary baseline inventory of shrub cover. In this study, we compare the accuracy of fractional shrub canopy (> 0.5 m tall) maps derived from multi-spectral, multi-angular, and multi-temporal datasets from Landsat imagery at 30 m spatial resolution, Moderate Resolution Imaging SpectroRadiometer (MODIS) imagery at 250 m and 500 m spatial resolution, and MultiAngle Imaging Spectroradiometer (MISR) imagery at 275 m spatial resolution for a 1067 km2 study area in Arctic Alaska. The study area is centered at 69 ??N, ranges in elevation from 130 to 770 m, is composed primarily of rolling topography with gentle slopes less than 10??, and is free of glaciers and perennial snow cover. Shrubs > 0.5 m in height cover 2.9% of the study area and are primarily confined to patches associated with specific landscape features. Reference fractional shrub canopy is determined from in situ shrub canopy measurements and a high spatial resolution IKONOS image swath. Regression tree models are constructed to estimate fractional canopy cover at 250 m using different combinations of input data from Landsat, MODIS, and MISR. Results indicate that multi-spectral data provide substantially more accurate estimates of fractional shrub canopy cover than multi-angular or multi-temporal data. Higher spatial resolution datasets also provide more accurate estimates of fractional shrub canopy cover (aggregated to moderate spatial resolutions) than lower spatial resolution datasets, an expected result for a study area where most shrub cover is concentrated in narrow patches associated with rivers, drainages, and slopes. Including the middle infrared bands available from Landsat and MODIS in the regression tree models (in addition to the four standard visible and near-infrared spectral bands) typically results in a slight boost in accuracy. Including the multi-angular red band data available from MISR in the regression tree models, however, typically boosts accuracy more substantially, resulting in moderate resolution fractional shrub canopy estimates approaching the accuracy of estimates derived from the much higher spatial resolution Landsat sensor. Given the poor availability of snow and cloud-free Landsat scenes in many areas of the Arctic and the promising results demonstrated here by the MISR sensor, MISR may be the best choice for large area fractional shrub canopy mapping in the Alaskan Arctic for the period 2000-2009.

Fusion of multi-source remote sensing data for agriculture monitoring tasks

NASA Astrophysics Data System (ADS)

Skakun, S.; Franch, B.; Vermote, E.; Roger, J. C.; Becker Reshef, I.; Justice, C. O.; Masek, J. G.; Murphy, E.

2016-12-01

Remote sensing data is essential source of information for enabling monitoring and quantification of crop state at global and regional scales. Crop mapping, state assessment, area estimation and yield forecasting are the main tasks that are being addressed within GEO-GLAM. Efficiency of agriculture monitoring can be improved when heterogeneous multi-source remote sensing datasets are integrated. Here, we present several case studies of utilizing MODIS, Landsat-8 and Sentinel-2 data along with meteorological data (growing degree days - GDD) for winter wheat yield forecasting, mapping and area estimation. Archived coarse spatial resolution data, such as MODIS, VIIRS and AVHRR, can provide daily global observations that coupled with statistical data on crop yield can enable the development of empirical models for timely yield forecasting at national level. With the availability of high-temporal and high spatial resolution Landsat-8 and Sentinel-2A imagery, course resolution empirical yield models can be downscaled to provide yield estimates at regional and field scale. In particular, we present the case study of downscaling the MODIS CMG based generalized winter wheat yield forecasting model to high spatial resolution data sets, namely harmonized Landsat-8 - Sentinel-2A surface reflectance product (HLS). Since the yield model requires corresponding in season crop masks, we propose an automatic approach to extract winter crop maps from MODIS NDVI and MERRA2 derived GDD using Gaussian mixture model (GMM). Validation for the state of Kansas (US) and Ukraine showed that the approach can yield accuracies > 90% without using reference (ground truth) data sets. Another application of yearly derived winter crop maps is their use for stratification purposes within area frame sampling for crop area estimation. In particular, one can simulate the dependence of error (coefficient of variation) on the number of samples and strata size. This approach was used for estimating the area of winter crops in Ukraine for 2013-2016. The GMM-GDD approach is further extended for HLS data to provide automatic winter crop mapping at 30 m resolution for crop yield model and area estimation. In case of persistent cloudiness, addition of Sentinel-1A synthetic aperture radar (SAR) images is explored for automatic winter crop mapping.

An approach for mapping large-area impervious surfaces: Synergistic use of Landsat-7 ETM+ and high spatial resolution imagery

USGS Publications Warehouse

Yang, Limin; Huang, Chengquan; Homer, Collin G.; Wylie, Bruce K.; Coan, Michael

2003-01-01

A wide range of urban ecosystem studies, including urban hydrology, urban climate, land use planning, and resource management, require current and accurate geospatial data of urban impervious surfaces. We developed an approach to quantify urban impervious surfaces as a continuous variable by using multisensor and multisource datasets. Subpixel percent impervious surfaces at 30-m resolution were mapped using a regression tree model. The utility, practicality, and affordability of the proposed method for large-area imperviousness mapping were tested over three spatial scales (Sioux Falls, South Dakota, Richmond, Virginia, and the Chesapeake Bay areas of the United States). Average error of predicted versus actual percent impervious surface ranged from 8.8 to 11.4%, with correlation coefficients from 0.82 to 0.91. The approach is being implemented to map impervious surfaces for the entire United States as one of the major components of the circa 2000 national land cover database.

Portable non-invasive brain-computer interface: challenges and opportunities of optical modalities

NASA Astrophysics Data System (ADS)

Scholl, Clara A.; Hendrickson, Scott M.; Swett, Bruce A.; Fitch, Michael J.; Walter, Erich C.; McLoughlin, Michael P.; Chevillet, Mark A.; Blodgett, David W.; Hwang, Grace M.

2017-05-01

The development of portable non-invasive brain computer interface technologies with higher spatio-temporal resolution has been motivated by the tremendous success seen with implanted devices. This talk will discuss efforts to overcome several major obstacles to viability including approaches that promise to improve spatial and temporal resolution. Optical approaches in particular will be highlighted and the potential benefits of both Blood-Oxygen Level Dependent (BOLD) and Fast Optical Signal (FOS) will be discussed. Early-stage research into the correlations between neural activity and FOS will be explored.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kumar, Jitendra; Hoffman, Forrest M.; Hargrove, William W.

This data set contain global gridded surfaces of Gross Primary Productivity (GPP) at 2 arc minute (approximately 4 km) spatial resolution monthly for the period of 2000-2014 derived from FLUXNET2015 (released July 12, 2016) observations using a representativeness based upscaling approach.

Non-invasive measurement of frog skin reflectivity in high spatial resolution using a dual hyperspectral approach.

PubMed

Pinto, Francisco; Mielewczik, Michael; Liebisch, Frank; Walter, Achim; Greven, Hartmut; Rascher, Uwe

2013-01-01

Most spectral data for the amphibian integument are limited to the visible spectrum of light and have been collected using point measurements with low spatial resolution. In the present study a dual camera setup consisting of two push broom hyperspectral imaging systems was employed, which produces reflectance images between 400 and 2500 nm with high spectral and spatial resolution and a high dynamic range. We briefly introduce the system and document the high efficiency of this technique analyzing exemplarily the spectral reflectivity of the integument of three arboreal anuran species (Litoria caerulea, Agalychnis callidryas and Hyla arborea), all of which appear green to the human eye. The imaging setup generates a high number of spectral bands within seconds and allows non-invasive characterization of spectral characteristics with relatively high working distance. Despite the comparatively uniform coloration, spectral reflectivity between 700 and 1100 nm differed markedly among the species. In contrast to H. arborea, L. caerulea and A. callidryas showed reflection in this range. For all three species, reflectivity above 1100 nm is primarily defined by water absorption. Furthermore, the high resolution allowed examining even small structures such as fingers and toes, which in A. callidryas showed an increased reflectivity in the near infrared part of the spectrum. Hyperspectral imaging was found to be a very useful alternative technique combining the spectral resolution of spectrometric measurements with a higher spatial resolution. In addition, we used Digital Infrared/Red-Edge Photography as new simple method to roughly determine the near infrared reflectivity of frog specimens in field, where hyperspectral imaging is typically difficult.

An integrated approach for high spatial resolution mapping of water and carbon fluxes using multi-sensor data

USDA-ARS?s Scientific Manuscript database

In the last few years, modeling of surface processes, such as water and carbon balances, vegetation growth and energy budgets, has focused on integrated approaches that combine aspects of hydrology, biology and meteorology into unified analyses. In this context, remotely sensed data often have a cor...

Surface Wave Tomography with Spatially Varying Smoothing Based on Continuous Model Regionalization

NASA Astrophysics Data System (ADS)

Liu, Chuanming; Yao, Huajian

2017-03-01

Surface wave tomography based on continuous regionalization of model parameters is widely used to invert for 2-D phase or group velocity maps. An inevitable problem is that the distribution of ray paths is far from homogeneous due to the spatially uneven distribution of stations and seismic events, which often affects the spatial resolution of the tomographic model. We present an improved tomographic method with a spatially varying smoothing scheme that is based on the continuous regionalization approach. The smoothness of the inverted model is constrained by the Gaussian a priori model covariance function with spatially varying correlation lengths based on ray path density. In addition, a two-step inversion procedure is used to suppress the effects of data outliers on tomographic models. Both synthetic and real data are used to evaluate this newly developed tomographic algorithm. In the synthetic tests, when the contrived model has different scales of anomalies but with uneven ray path distribution, we compare the performance of our spatially varying smoothing method with the traditional inversion method, and show that the new method is capable of improving the recovery in regions of dense ray sampling. For real data applications, the resulting phase velocity maps of Rayleigh waves in SE Tibet produced using the spatially varying smoothing method show similar features to the results with the traditional method. However, the new results contain more detailed structures and appears to better resolve the amplitude of anomalies. From both synthetic and real data tests we demonstrate that our new approach is useful to achieve spatially varying resolution when used in regions with heterogeneous ray path distribution.

Multispectral image enhancement processing for microsat-borne imager

NASA Astrophysics Data System (ADS)

Sun, Jianying; Tan, Zheng; Lv, Qunbo; Pei, Linlin

2017-10-01

With the rapid development of remote sensing imaging technology, the micro satellite, one kind of tiny spacecraft, appears during the past few years. A good many studies contribute to dwarfing satellites for imaging purpose. Generally speaking, micro satellites weigh less than 100 kilograms, even less than 50 kilograms, which are slightly larger or smaller than the common miniature refrigerators. However, the optical system design is hard to be perfect due to the satellite room and weight limitation. In most cases, the unprocessed data captured by the imager on the microsatellite cannot meet the application need. Spatial resolution is the key problem. As for remote sensing applications, the higher spatial resolution of images we gain, the wider fields we can apply them. Consequently, how to utilize super resolution (SR) and image fusion to enhance the quality of imagery deserves studying. Our team, the Key Laboratory of Computational Optical Imaging Technology, Academy Opto-Electronics, is devoted to designing high-performance microsat-borne imagers and high-efficiency image processing algorithms. This paper addresses a multispectral image enhancement framework for space-borne imagery, jointing the pan-sharpening and super resolution techniques to deal with the spatial resolution shortcoming of microsatellites. We test the remote sensing images acquired by CX6-02 satellite and give the SR performance. The experiments illustrate the proposed approach provides high-quality images.

Simulation of population-based commuter exposure to NO₂ using different air pollution models.

PubMed

Ragettli, Martina S; Tsai, Ming-Yi; Braun-Fahrländer, Charlotte; de Nazelle, Audrey; Schindler, Christian; Ineichen, Alex; Ducret-Stich, Regina E; Perez, Laura; Probst-Hensch, Nicole; Künzli, Nino; Phuleria, Harish C

2014-05-12

We simulated commuter routes and long-term exposure to traffic-related air pollution during commute in a representative population sample in Basel (Switzerland), and evaluated three air pollution models with different spatial resolution for estimating commute exposures to nitrogen dioxide (NO2) as a marker of long-term exposure to traffic-related air pollution. Our approach includes spatially and temporally resolved data on actual commuter routes, travel modes and three air pollution models. Annual mean NO2 commuter exposures were similar between models. However, we found more within-city and within-subject variability in annual mean (±SD) NO2 commuter exposure with a high resolution dispersion model (40 ± 7 µg m(-3), range: 21-61) than with a dispersion model with a lower resolution (39 ± 5 µg m(-3); range: 24-51), and a land use regression model (41 ± 5 µg m(-3); range: 24-54). Highest median cumulative exposures were calculated along motorized transport and bicycle routes, and the lowest for walking. For estimating commuter exposure within a city and being interested also in small-scale variability between roads, a model with a high resolution is recommended. For larger scale epidemiological health assessment studies, models with a coarser spatial resolution are likely sufficient, especially when study areas include suburban and rural areas.

Cross-Scale Molecular Analysis of Chemical Heterogeneity in Shale Rocks

DOE PAGES

Hao, Zhao; Bechtel, Hans A.; Kneafsey, Timothy; ...

2018-02-07

The organic and mineralogical heterogeneity in shale at micrometer and nanometer spatial scales contributes to the quality of gas reserves, gas flow mechanisms and gas production. Here, we demonstrate two molecular imaging approaches based on infrared spectroscopy to obtain mineral and kerogen information at these mesoscale spatial resolutions in large-sized shale rock samples. The first method is a modified microscopic attenuated total reflectance measurement that utilizes a large germanium hemisphere combined with a focal plane array detector to rapidly capture chemical images of shale rock surfaces spanning hundreds of micrometers with micrometer spatial resolution. The second method, synchrotron infrared nano-spectroscopy,more » utilizes a metallic atomic force microscope tip to obtain chemical images of micrometer dimensions but with nanometer spatial resolution. This chemically "deconvoluted" imaging at the nano-pore scale is then used to build a machine learning model to generate a molecular distribution map across scales with a spatial span of 1000 times, which enables high-throughput geochemical characterization in greater details across the nano-pore and micro-grain scales and allows us to identify co-localization of mineral phases with chemically distinct organics and even with gas phase sorbents. Finally, this characterization is fundamental to understand mineral and organic compositions affecting the behavior of shales.« less

Cross-Scale Molecular Analysis of Chemical Heterogeneity in Shale Rocks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hao, Zhao; Bechtel, Hans A.; Kneafsey, Timothy

The organic and mineralogical heterogeneity in shale at micrometer and nanometer spatial scales contributes to the quality of gas reserves, gas flow mechanisms and gas production. Here, we demonstrate two molecular imaging approaches based on infrared spectroscopy to obtain mineral and kerogen information at these mesoscale spatial resolutions in large-sized shale rock samples. The first method is a modified microscopic attenuated total reflectance measurement that utilizes a large germanium hemisphere combined with a focal plane array detector to rapidly capture chemical images of shale rock surfaces spanning hundreds of micrometers with micrometer spatial resolution. The second method, synchrotron infrared nano-spectroscopy,more » utilizes a metallic atomic force microscope tip to obtain chemical images of micrometer dimensions but with nanometer spatial resolution. This chemically "deconvoluted" imaging at the nano-pore scale is then used to build a machine learning model to generate a molecular distribution map across scales with a spatial span of 1000 times, which enables high-throughput geochemical characterization in greater details across the nano-pore and micro-grain scales and allows us to identify co-localization of mineral phases with chemically distinct organics and even with gas phase sorbents. Finally, this characterization is fundamental to understand mineral and organic compositions affecting the behavior of shales.« less

Digital micromirror device-based laser-illumination Fourier ptychographic microscopy

PubMed Central

Kuang, Cuifang; Ma, Ye; Zhou, Renjie; Lee, Justin; Barbastathis, George; Dasari, Ramachandra R.; Yaqoob, Zahid; So, Peter T. C.

2015-01-01

We report a novel approach to Fourier ptychographic microscopy (FPM) by using a digital micromirror device (DMD) and a coherent laser source (532 nm) for generating spatially modulated sample illumination. Previously demonstrated FPM systems are all based on partially-coherent illumination, which offers limited throughput due to insufficient brightness. Our FPM employs a high power coherent laser source to enable shot-noise limited high-speed imaging. For the first time, a digital micromirror device (DMD), imaged onto the back focal plane of the illumination objective, is used to generate spatially modulated sample illumination field for ptychography. By coding the on/off states of the micromirrors, the illumination plane wave angle can be varied at speeds more than 4 kHz. A set of intensity images, resulting from different oblique illuminations, are used to numerically reconstruct one high-resolution image without obvious laser speckle. Experiments were conducted using a USAF resolution target and a fiber sample, demonstrating high-resolution imaging capability of our system. We envision that our approach, if combined with a coded-aperture compressive-sensing algorithm, will further improve the imaging speed in DMD-based FPM systems. PMID:26480361

Digital micromirror device-based laser-illumination Fourier ptychographic microscopy.

PubMed

Kuang, Cuifang; Ma, Ye; Zhou, Renjie; Lee, Justin; Barbastathis, George; Dasari, Ramachandra R; Yaqoob, Zahid; So, Peter T C

2015-10-19

We report a novel approach to Fourier ptychographic microscopy (FPM) by using a digital micromirror device (DMD) and a coherent laser source (532 nm) for generating spatially modulated sample illumination. Previously demonstrated FPM systems are all based on partially-coherent illumination, which offers limited throughput due to insufficient brightness. Our FPM employs a high power coherent laser source to enable shot-noise limited high-speed imaging. For the first time, a digital micromirror device (DMD), imaged onto the back focal plane of the illumination objective, is used to generate spatially modulated sample illumination field for ptychography. By coding the on/off states of the micromirrors, the illumination plane wave angle can be varied at speeds more than 4 kHz. A set of intensity images, resulting from different oblique illuminations, are used to numerically reconstruct one high-resolution image without obvious laser speckle. Experiments were conducted using a USAF resolution target and a fiber sample, demonstrating high-resolution imaging capability of our system. We envision that our approach, if combined with a coded-aperture compressive-sensing algorithm, will further improve the imaging speed in DMD-based FPM systems.

Pressure spectra from single-snapshot tomographic PIV

NASA Astrophysics Data System (ADS)

Schneiders, Jan F. G.; Avallone, Francesco; Pröbsting, Stefan; Ragni, Daniele; Scarano, Fulvio

2018-03-01

The power spectral density and coherence of temporal pressure fluctuations are obtained from low-repetition-rate tomographic PIV measurements. This is achieved by extension of recent single-snapshot pressure evaluation techniques based upon the Taylor's hypothesis (TH) of frozen turbulence and vortex-in-cell (VIC) simulation. Finite time marching of the measured instantaneous velocity fields is performed using TH and VIC. Pressure is calculated from the resulting velocity time series. Because of the theoretical limitations, the finite time marching can be performed until the measured flow structures are convected out of the measurement volume. This provides a lower limit of resolvable frequency range. An upper limit is given by the spatial resolution of the measurements. Finite time-marching approaches are applied to low-repetition-rate tomographic PIV data of the flow past a straight trailing edge at 10 m/s. Reference results of the power spectral density and coherence are obtained from surface pressure transducers. In addition, the results are compared to state-of-the-art experimental data obtained from time-resolved tomographic PIV performed at 10 kHz. The time-resolved approach suffers from low spatial resolution and limited maximum acquisition frequency because of hardware limitations. Additionally, these approaches strongly depend upon the time kernel length chosen for pressure evaluation. On the other hand, the finite time-marching approaches make use of low-repetition-rate tomographic PIV measurements that offer higher spatial resolution. Consequently, increased accuracy of the power spectral density and coherence of pressure fluctuations are obtained in the high-frequency range, in comparison to the time-resolved measurements. The approaches based on TH and VIC are found to perform similarly in the high-frequency range. At lower frequencies, TH is found to underestimate coherence and intensity of the pressure fluctuations in comparison to time-resolved PIV and the microphone reference data. The VIC-based approach, on the other hand, returns results on the order of the reference.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Jiali; Swati, F. N. U.; Stein, Michael L.

Regional climate models (RCMs) are a standard tool for downscaling climate forecasts to finer spatial scales. The evaluation of RCMs against observational data is an important step in building confidence in the use of RCMs for future prediction. In addition to model performance in climatological means and marginal distributions, a model’s ability to capture spatio-temporal relationships is important. This study develops two approaches: (1) spatial correlation/variogram for a range of spatial lags, with total monthly precipitation and non-seasonal precipitation components used to assess the spatial variations of precipitation; and (2) spatio-temporal correlation for a wide range of distances, directions, andmore » time lags, with daily precipitation occurrence used to detect the dynamic features of precipitation. These measures of spatial and spatio-temporal dependence are applied to a high-resolution RCM run and to the National Center for Environmental Prediction (NCEP)-U.S. Department of Energy (DOE) AMIP II reanalysis data (NCEP-R2), which provides initial and lateral boundary conditions for the RCM. The RCM performs better than NCEP-R2 in capturing both the spatial variations of total and non-seasonal precipitation components and the spatio-temporal correlations of daily precipitation occurrences, which are related to dynamic behaviors of precipitating systems. The improvements are apparent not just at resolutions finer than that of NCEP-R2, but also when the RCM and observational data are aggregated to the resolution of NCEP-R2.« less

Superresolved digital in-line holographic microscopy for high-resolution lensless biological imaging

NASA Astrophysics Data System (ADS)

Micó, Vicente; Zalevsky, Zeev

2010-07-01

Digital in-line holographic microscopy (DIHM) is a modern approach capable of achieving micron-range lateral and depth resolutions in three-dimensional imaging. DIHM in combination with numerical imaging reconstruction uses an extremely simplified setup while retaining the advantages provided by holography with enhanced capabilities derived from algorithmic digital processing. We introduce superresolved DIHM incoming from time and angular multiplexing of the sample spatial frequency information and yielding in the generation of a synthetic aperture (SA). The SA expands the cutoff frequency of the imaging system, allowing submicron resolutions in both transversal and axial directions. The proposed approach can be applied when imaging essentially transparent (low-concentration dilutions) and static (slow dynamics) samples. Validation of the method for both a synthetic object (U.S. Air Force resolution test) to quantify the resolution improvement and a biological specimen (sperm cells biosample) are reported showing the generation of high synthetic numerical aperture values working without lenses.

A new PET detector concept for compact preclinical high-resolution hybrid MR-PET

NASA Astrophysics Data System (ADS)

Berneking, Arne; Gola, Alberto; Ferri, Alessandro; Finster, Felix; Rucatti, Daniele; Paternoster, Giovanni; Jon Shah, N.; Piemonte, Claudio; Lerche, Christoph

2018-04-01

This work presents a new PET detector concept for compact preclinical hybrid MR-PET. The detector concept is based on Linearly-Graded SiPM produced with current FBK RGB-HD technology. One 7.75 mm x 7.75 mm large sensor chip is coupled with optical grease to a black coated 8 mm x 8 mm large and 3 mm thick monolithic LYSO crystal. The readout is obtained from four readout channels with the linear encoding based on integrated resistors and the Center of Gravity approach. To characterize the new detector concept, the spatial and energy resolutions were measured. Therefore, the measurement setup was prepared to radiate a collimated beam to 25 different points perpendicular to the monolithic scintillator crystal. Starting in the center point of the crystal at 0 mm / 0 mm and sampling a grid with a pitch of 1.75 mm, all significant points of the detector were covered by the collimator beam. The measured intrinsic spatial resolution (FWHM) was 0.74 +/- 0.01 mm in x- and 0.69 +/- 0.01 mm in the y-direction at the center of the detector. At the same point, the measured energy resolution (FWHM) was 13.01 +/- 0.05 %. The mean intrinsic spatial resolution (FWHM) over the whole detector was 0.80 +/- 0.28 mm in x- and 0.72 +/- 0.19 mm in y-direction. The energy resolution (FWHM) of the detector was between 13 and 17.3 % with an average energy resolution of 15.7 +/- 1.0 %. Due to the reduced thickness, the sensitivity of this gamma detector is low but still higher than pixelated designs with the same thickness due to the monolithic crystals. Combining compact design, high spatial resolution, and high sensitivity, the detector concept is particularly suitable for applications where the scanner bore size is limited and high resolution is required - as is the case in small animal hybrid MR-PET.

High resolution decadal precipitation predictions over the continental United States for impacts assessment

NASA Astrophysics Data System (ADS)

Salvi, Kaustubh; Villarini, Gabriele; Vecchi, Gabriel A.

2017-10-01

Unprecedented alterations in precipitation characteristics over the last century and especially in the last two decades have posed serious socio-economic problems to society in terms of hydro-meteorological extremes, in particular flooding and droughts. The origin of these alterations has its roots in changing climatic conditions; however, its threatening implications can only be dealt with through meticulous planning that is based on realistic and skillful decadal precipitation predictions (DPPs). Skillful DPPs represent a very challenging prospect because of the complexities associated with precipitation predictions. Because of the limited skill and coarse spatial resolution, the DPPs provided by General Circulation Models (GCMs) fail to be directly applicable for impact assessment. Here, we focus on nine GCMs and quantify the seasonally and regionally averaged skill in DPPs over the continental United States. We address the problems pertaining to the limited skill and resolution by applying linear and kernel regression-based statistical downscaling approaches. For both the approaches, statistical relationships established over the calibration period (1961-1990) are applied to the retrospective and near future decadal predictions by GCMs to obtain DPPs at ∼4 km resolution. The skill is quantified across different metrics that evaluate potential skill, biases, long-term statistical properties, and uncertainty. Both the statistical approaches show improvements with respect to the raw GCM data, particularly in terms of the long-term statistical properties and uncertainty, irrespective of lead time. The outcome of the study is monthly DPPs from nine GCMs with 4-km spatial resolution, which can be used as a key input for impacts assessments.

Direct Visualization of Valence Electron Motion Using Strong-Field Photoelectron Holography

NASA Astrophysics Data System (ADS)

He, Mingrui; Li, Yang; Zhou, Yueming; Li, Min; Cao, Wei; Lu, Peixiang

2018-03-01

Watching the valence electron move in molecules on its intrinsic timescale has been one of the central goals of attosecond science and it requires measurements with subatomic spatial and attosecond temporal resolutions. The time-resolved photoelectron holography in strong-field tunneling ionization holds the promise to access this realm. However, it remains to be a challenging task hitherto. Here we reveal how the information of valence electron motion is encoded in the hologram of the photoelectron momentum distribution (PEMD) and develop a novel approach of retrieval. As a demonstration, applying it to the PEMDs obtained by solving the time-dependent Schrödinger equation for the prototypical molecule H2+ , the attosecond charge migration is directly visualized with picometer spatial and attosecond temporal resolutions. Our method represents a general approach for monitoring attosecond charge migration in more complex polyatomic and biological molecules, which is one of the central tasks in the newly emerging attosecond chemistry.

Super long viewing distance light homogeneous emitting three-dimensional display

NASA Astrophysics Data System (ADS)

Liao, Hongen

2015-04-01

Three-dimensional (3D) display technology has continuously been attracting public attention with the progress in today's 3D television and mature display technologies. The primary characteristics of conventional glasses-free autostereoscopic displays, such as spatial resolution, image depths, and viewing angle, are often limited due to the use of optical lenses or optical gratings. We present a 3D display using MEMS-scanning-mechanism-based light homogeneous emitting (LHE) approach and demonstrate that the display can directly generate an autostereoscopic 3D image without the need for optical lenses or gratings. The generated 3D image has the advantages of non-aberration and a high-definition spatial resolution, making it the first to exhibit animated 3D images with image depth of six meters. Our LHE 3D display approach can be used to generate a natural flat-panel 3D display with super long viewing distance and alternative real-time image update.

Hybrid cardiac imaging with MR-CAT scan: a feasibility study.

PubMed

Hillenbrand, C; Sandstede, J; Pabst, T; Hahn, D; Haase, A; Jakob, P M

2000-06-01

We demonstrate the feasibility of a new versatile hybrid imaging concept, the combined acquisition technique (CAT), for cardiac imaging. The cardiac CAT approach, which combines new methodology with existing technology, essentially integrates fast low-angle shot (FLASH) and echoplanar imaging (EPI) modules in a sequential fashion, whereby each acquisition module is employed with independently optimized imaging parameters. One important CAT sequence optimization feature is the ability to use different bandwidths for different acquisition modules. Twelve healthy subjects were imaged using three cardiac CAT acquisition strategies: a) CAT was used to reduce breath-hold duration times while maintaining constant spatial resolution; b) CAT was used to increase spatial resolution in a given breath-hold time; and c) single-heart beat CAT imaging was performed. The results obtained demonstrate the feasibility of cardiac imaging using the CAT approach and the potential of this technique to accelerate the imaging process with almost conserved image quality. Copyright 2000 Wiley-Liss, Inc.

Direct Visualization of Valence Electron Motion Using Strong-Field Photoelectron Holography.

PubMed

He, Mingrui; Li, Yang; Zhou, Yueming; Li, Min; Cao, Wei; Lu, Peixiang

2018-03-30

Watching the valence electron move in molecules on its intrinsic timescale has been one of the central goals of attosecond science and it requires measurements with subatomic spatial and attosecond temporal resolutions. The time-resolved photoelectron holography in strong-field tunneling ionization holds the promise to access this realm. However, it remains to be a challenging task hitherto. Here we reveal how the information of valence electron motion is encoded in the hologram of the photoelectron momentum distribution (PEMD) and develop a novel approach of retrieval. As a demonstration, applying it to the PEMDs obtained by solving the time-dependent Schrödinger equation for the prototypical molecule H_{2}^{+}, the attosecond charge migration is directly visualized with picometer spatial and attosecond temporal resolutions. Our method represents a general approach for monitoring attosecond charge migration in more complex polyatomic and biological molecules, which is one of the central tasks in the newly emerging attosecond chemistry.

Tip-enhanced Raman mapping with top-illumination AFM.

PubMed

Chan, K L Andrew; Kazarian, Sergei G

2011-04-29

Tip-enhanced Raman mapping is a powerful, emerging technique that offers rich chemical information and high spatial resolution. Currently, most of the successes in tip-enhanced Raman scattering (TERS) measurements are based on the inverted configuration where tips and laser are approaching the sample from opposite sides. This results in the limitation of measurement for transparent samples only. Several approaches have been developed to obtain tip-enhanced Raman mapping in reflection mode, many of which involve certain customisations of the system. We have demonstrated in this work that it is also possible to obtain TERS nano-images using an upright microscope (top-illumination) with a gold-coated Si atomic force microscope (AFM) cantilever without significant modification to the existing integrated AFM/Raman system. A TERS image of a single-walled carbon nanotube has been achieved with a spatial resolution of ∼ 20-50 nm, demonstrating the potential of this technique for studying non-transparent nanoscale materials.

Laser ablation surface-enhanced Raman microspectroscopy.

PubMed

Londero, Pablo S; Lombardi, John R; Leona, Marco

2013-06-04

Improved identification of trace organic compounds in complex matrixes is critical for a variety of fields such as material science, heritage science, and forensics. Surface-enhanced Raman scattering (SERS) is a vibrational spectroscopy technique that can attain single-molecule sensitivity and has been shown to complement mass spectrometry, but lacks widespread application without a robust method that utilizes the effect. We demonstrate a new, highly sensitive, and widely applicable approach to SERS analysis based on laser ablation in the presence of a tailored plasmonic substrate. We analyze several challenging compounds, including non-water-soluble pigments and dyed leather from an ancient Egyptian chariot, achieving sensitivity as high as 120 amol for a 1:1 signal-to-noise ratio and 5 μm spatial resolution. This represents orders of magnitude improvement in spatial resolution and sensitivity compared to those of other SERS approaches intended for widespread application, greatly increasing the applicability of SERS.

Blind image fusion for hyperspectral imaging with the directional total variation

NASA Astrophysics Data System (ADS)

Bungert, Leon; Coomes, David A.; Ehrhardt, Matthias J.; Rasch, Jennifer; Reisenhofer, Rafael; Schönlieb, Carola-Bibiane

2018-04-01

Hyperspectral imaging is a cutting-edge type of remote sensing used for mapping vegetation properties, rock minerals and other materials. A major drawback of hyperspectral imaging devices is their intrinsic low spatial resolution. In this paper, we propose a method for increasing the spatial resolution of a hyperspectral image by fusing it with an image of higher spatial resolution that was obtained with a different imaging modality. This is accomplished by solving a variational problem in which the regularization functional is the directional total variation. To accommodate for possible mis-registrations between the two images, we consider a non-convex blind super-resolution problem where both a fused image and the corresponding convolution kernel are estimated. Using this approach, our model can realign the given images if needed. Our experimental results indicate that the non-convexity is negligible in practice and that reliable solutions can be computed using a variety of different optimization algorithms. Numerical results on real remote sensing data from plant sciences and urban monitoring show the potential of the proposed method and suggests that it is robust with respect to the regularization parameters, mis-registration and the shape of the kernel.

Improving Secondary Ion Mass Spectrometry Image Quality with Image Fusion

PubMed Central

Tarolli, Jay G.; Jackson, Lauren M.; Winograd, Nicholas

2014-01-01

The spatial resolution of chemical images acquired with cluster secondary ion mass spectrometry (SIMS) is limited not only by the size of the probe utilized to create the images, but also by detection sensitivity. As the probe size is reduced to below 1 µm, for example, a low signal in each pixel limits lateral resolution due to counting statistics considerations. Although it can be useful to implement numerical methods to mitigate this problem, here we investigate the use of image fusion to combine information from scanning electron microscope (SEM) data with chemically resolved SIMS images. The advantage of this approach is that the higher intensity and, hence, spatial resolution of the electron images can help to improve the quality of the SIMS images without sacrificing chemical specificity. Using a pan-sharpening algorithm, the method is illustrated using synthetic data, experimental data acquired from a metallic grid sample, and experimental data acquired from a lawn of algae cells. The results show that up to an order of magnitude increase in spatial resolution is possible to achieve. A cross-correlation metric is utilized for evaluating the reliability of the procedure. PMID:24912432

High-Resolution Genuinely Multidimensional Solution of Conservation Laws by the Space-Time Conservation Element and Solution Element Method

NASA Technical Reports Server (NTRS)

Himansu, Ananda; Chang, Sin-Chung; Yu, Sheng-Tao; Wang, Xiao-Yen; Loh, Ching-Yuen; Jorgenson, Philip C. E.

1999-01-01

In this overview paper, we review the basic principles of the method of space-time conservation element and solution element for solving the conservation laws in one and two spatial dimensions. The present method is developed on the basis of local and global flux conservation in a space-time domain, in which space and time are treated in a unified manner. In contrast to the modern upwind schemes, the approach here does not use the Riemann solver and the reconstruction procedure as the building blocks. The drawbacks of the upwind approach, such as the difficulty of rationally extending the 1D scalar approach to systems of equations and particularly to multiple dimensions is here contrasted with the uniformity and ease of generalization of the Conservation Element and Solution Element (CE/SE) 1D scalar schemes to systems of equations and to multiple spatial dimensions. The assured compatibility with the simplest type of unstructured meshes, and the uniquely simple nonreflecting boundary conditions of the present method are also discussed. The present approach has yielded high-resolution shocks, rarefaction waves, acoustic waves, vortices, ZND detonation waves, and shock/acoustic waves/vortices interactions. Moreover, since no directional splitting is employed, numerical resolution of two-dimensional calculations is comparable to that of the one-dimensional calculations. Some sample applications displaying the strengths and broad applicability of the CE/SE method are reviewed.

Spatially detailed retrievals of spring phenology from single-season high-resolution image time series

NASA Astrophysics Data System (ADS)

Vrieling, Anton; Skidmore, Andrew K.; Wang, Tiejun; Meroni, Michele; Ens, Bruno J.; Oosterbeek, Kees; O'Connor, Brian; Darvishzadeh, Roshanak; Heurich, Marco; Shepherd, Anita; Paganini, Marc

2017-07-01

Vegetation indices derived from satellite image time series have been extensively used to estimate the timing of phenological events like season onset. Medium spatial resolution (≥250 m) satellite sensors with daily revisit capability are typically employed for this purpose. In recent years, phenology is being retrieved at higher resolution (≤30 m) in response to increasing availability of high-resolution satellite data. To overcome the reduced acquisition frequency of such data, previous attempts involved fusion between high- and medium-resolution data, or combinations of multi-year acquisitions in a single phenological reconstruction. The objectives of this study are to demonstrate that phenological parameters can now be retrieved from single-season high-resolution time series, and to compare these retrievals against those derived from multi-year high-resolution and single-season medium-resolution satellite data. The study focuses on the island of Schiermonnikoog, the Netherlands, which comprises a highly-dynamic saltmarsh, dune vegetation, and agricultural land. Combining NDVI series derived from atmospherically-corrected images from RapidEye (5 m-resolution) and the SPOT5 Take5 experiment (10m-resolution) acquired between March and August 2015, phenological parameters were estimated using a function fitting approach. We then compared results with phenology retrieved from four years of 30 m Landsat 8 OLI data, and single-year 100 m Proba-V and 250 m MODIS temporal composites of the same period. Retrieved phenological parameters from combined RapidEye/SPOT5 displayed spatially consistent results and a large spatial variability, providing complementary information to existing vegetation community maps. Retrievals that combined four years of Landsat observations into a single synthetic year were affected by the inclusion of years with warmer spring temperatures, whereas adjustment of the average phenology to 2015 observations was only feasible for a few pixels due to cloud cover around phenological transition dates. The Proba-V and MODIS phenology retrievals scaled poorly relative to their high-resolution equivalents, indicating that medium-resolution phenology retrievals need to be interpreted with care, particularly in landscapes with fine-scale land cover variability.

An analysis of tree mortality using high resolution remotely-sensed data for mixed-conifer forests in San Diego county

NASA Astrophysics Data System (ADS)

Freeman, Mary Pyott

ABSTRACT An Analysis of Tree Mortality Using High Resolution Remotely-Sensed Data for Mixed-Conifer Forests in San Diego County by Mary Pyott Freeman The montane mixed-conifer forests of San Diego County are currently experiencing extensive tree mortality, which is defined as dieback where whole stands are affected. This mortality is likely the result of the complex interaction of many variables, such as altered fire regimes, climatic conditions such as drought, as well as forest pathogens and past management strategies. Conifer tree mortality and its spatial pattern and change over time were examined in three components. In component 1, two remote sensing approaches were compared for their effectiveness in delineating dead trees, a spatial contextual approach and an OBIA (object based image analysis) approach, utilizing various dates and spatial resolutions of airborne image data. For each approach transforms and masking techniques were explored, which were found to improve classifications, and an object-based assessment approach was tested. In component 2, dead tree maps produced by the most effective techniques derived from component 1 were utilized for point pattern and vector analyses to further understand spatio-temporal changes in tree mortality for the years 1997, 2000, 2002, and 2005 for three study areas: Palomar, Volcan and Laguna mountains. Plot-based fieldwork was conducted to further assess mortality patterns. Results indicate that conifer mortality was significantly clustered, increased substantially between 2002 and 2005, and was non-random with respect to tree species and diameter class sizes. In component 3, multiple environmental variables were used in Generalized Linear Model (GLM-logistic regression) and decision tree classifier model development, revealing the importance of climate and topographic factors such as precipitation and elevation, in being able to predict areas of high risk for tree mortality. The results from this study highlight the importance of multi-scale spatial as well as temporal analyses, in order to understand mixed-conifer forest structure, dynamics, and processes of decline, which can lead to more sustainable management of forests with continued natural and anthropogenic disturbance.

Compensation of temporal and spatial dispersion for multiphoton acousto-optic laser-scanning microscopy

NASA Astrophysics Data System (ADS)

Iyer, Vijay; Saggau, Peter

2003-10-01

In laser-scanning microscopy, acousto-optic (AO) deflection provides a means to quickly position a laser beam to random locations throughout the field-of-view. Compared to conventional laser-scanning using galvanometer-driven mirrors, this approach increases the frame rate and signal-to-noise ratio, and reduces time spent illuminating sites of no interest. However, random-access AO scanning has not yet been combined with multi-photon microscopy, primarily because the femtosecond laser pulses employed are subject to significant amounts of both spatial and temporal dispersion upon propagation through common AO materials. Left uncompensated, spatial dispersion reduces the microscope"s spatial resolution while temporal dispersion reduces the multi-photon excitation efficacy. In previous work, we have demonstrated, 1) the efficacy of a single diffraction grating scheme which reduces the spatial dispersion at least 3-fold throughout the field-of-view, and 2) the use of a novel stacked-prism pre-chirper for compensating the temporal dispersion of a pair of AODs using a shorter mechanical path length (2-4X) than standard prism-pair arrangements. In this work, we demonstrate for the first time the use of these compensation approaches with a custom-made large-area slow-shear TeO2 AOD specifically suited for the development of a high-resolution 2-D random-access AO scanning multi-photon laser-scanning microscope (AO-MPLSM).

Proceedings of the 2004 High Spatial Resolution Commercial Imagery Workshop

NASA Technical Reports Server (NTRS)

2006-01-01

Topics covered include: NASA Applied Sciences Program; USGS Land Remote Sensing: Overview; QuickBird System Status and Product Overview; ORBIMAGE Overview; IKONOS 2004 Calibration and Validation Status; OrbView-3 Spatial Characterization; On-Orbit Modulation Transfer Function (MTF) Measurement of QuickBird; Spatial Resolution Characterization for QuickBird Image Products 2003-2004 Season; Image Quality Evaluation of QuickBird Super Resolution and Revisit of IKONOS: Civil and Commercial Application Project (CCAP); On-Orbit System MTF Measurement; QuickBird Post Launch Geopositional Characterization Update; OrbView-3 Geometric Calibration and Geopositional Accuracy; Geopositional Statistical Methods; QuickBird and OrbView-3 Geopositional Accuracy Assessment; Initial On-Orbit Spatial Resolution Characterization of OrbView-3 Panchromatic Images; Laboratory Measurement of Bidirectional Reflectance of Radiometric Tarps; Stennis Space Center Verification and Validation Capabilities; Joint Agency Commercial Imagery Evaluation (JACIE) Team; Adjacency Effects in High Resolution Imagery; Effect of Pulse Width vs. GSD on MTF Estimation; Camera and Sensor Calibration at the USGS; QuickBird Geometric Verification; Comparison of MODTRAN to Heritage-based Results in Vicarious Calibration at University of Arizona; Using Remotely Sensed Imagery to Determine Impervious Surface in Sioux Falls, South Dakota; Estimating Sub-Pixel Proportions of Sagebrush with a Regression Tree; How Do YOU Use the National Land Cover Dataset?; The National Map Hazards Data Distribution System; Recording a Troubled World; What Does This-Have to Do with This?; When Can a Picture Save a Thousand Homes?; InSAR Studies of Alaska Volcanoes; Earth Observing-1 (EO-1) Data Products; Improving Access to the USGS Aerial Film Collections: High Resolution Scanners; Improving Access to the USGS Aerial Film Collections: Phoenix Digitizing System Product Distribution; System and Product Characterization: Issues Approach; Innovative Approaches to Analysis of Lidar Data for the National Map; Changes in Imperviousness near Military Installations; Geopositional Accuracy Evaluations of QuickBird and OrbView-3: Civil and Commercial Applications Project (CCAP); Geometric Accuracy Assessment: OrbView ORTHO Products; QuickBird Radiometric Calibration Update; OrbView-3 Radiometric Calibration; QuickBird Radiometric Characterization; NASA Radiometric Characterization; Establishing and Verifying the Traceability of Remote-Sensing Measurements to International Standards; QuickBird Applications; Airport Mapping and Perpetual Monitoring Using IKONOS; OrbView-3 Relative Accuracy Results and Impacts on Exploitation and Accuracy Improvement; Using Remotely Sensed Imagery to Determine Impervious Surface in Sioux Falls, South Dakota; Applying High-Resolution Satellite Imagery and Remotely Sensed Data to Local Government Applications: Sioux Falls, South Dakota; Automatic Co-Registration of QuickBird Data for Change Detection Applications; Developing Coastal Surface Roughness Maps Using ASTER and QuickBird Data Sources; Automated, Near-Real Time Cloud and Cloud Shadow Detection in High Resolution VNIR Imagery; Science Applications of High Resolution Imagery at the USGS EROS Data Center; Draft Plan for Characterizing Commercial Data Products in Support of Earth Science Research; Atmospheric Correction Prototype Algorithm for High Spatial Resolution Multispectral Earth Observing Imaging Systems; Determining Regional Arctic Tundra Carbon Exchange: A Bottom-Up Approach; Using IKONOS Imagery to Assess Impervious Surface Area, Riparian Buffers and Stream Health in the Mid-Atlantic Region; Commercial Remote Sensing Space Policy Civil Implementation Update; USGS Commercial Remote Sensing Data Contracts (CRSDC); and Commercial Remote Sensing Space Policy (CRSSP): Civil Near-Term Requirements Collection Update.

Using Remotely Sensed Information for Near Real-Time Landslide Hazard Assessment

NASA Technical Reports Server (NTRS)

Kirschbaum, Dalia; Adler, Robert; Peters-Lidard, Christa

2013-01-01

The increasing availability of remotely sensed precipitation and surface products provides a unique opportunity to explore how landslide susceptibility and hazard assessment may be approached at larger spatial scales with higher resolution remote sensing products. A prototype global landslide hazard assessment framework has been developed to evaluate how landslide susceptibility and satellite-derived precipitation estimates can be used to identify potential landslide conditions in near-real time. Preliminary analysis of this algorithm suggests that forecasting errors are geographically variable due to the resolution and accuracy of the current susceptibility map and the application of satellite-based rainfall estimates. This research is currently working to improve the algorithm through considering higher spatial and temporal resolution landslide susceptibility information and testing different rainfall triggering thresholds, antecedent rainfall scenarios, and various surface products at regional and global scales.

Puzzle Imaging: Using Large-Scale Dimensionality Reduction Algorithms for Localization.

PubMed

Glaser, Joshua I; Zamft, Bradley M; Church, George M; Kording, Konrad P

2015-01-01

Current high-resolution imaging techniques require an intact sample that preserves spatial relationships. We here present a novel approach, "puzzle imaging," that allows imaging a spatially scrambled sample. This technique takes many spatially disordered samples, and then pieces them back together using local properties embedded within the sample. We show that puzzle imaging can efficiently produce high-resolution images using dimensionality reduction algorithms. We demonstrate the theoretical capabilities of puzzle imaging in three biological scenarios, showing that (1) relatively precise 3-dimensional brain imaging is possible; (2) the physical structure of a neural network can often be recovered based only on the neural connectivity matrix; and (3) a chemical map could be reproduced using bacteria with chemosensitive DNA and conjugative transfer. The ability to reconstruct scrambled images promises to enable imaging based on DNA sequencing of homogenized tissue samples.

A semiparametric spatio-temporal model for solar irradiance data

DOE PAGES

Patrick, Joshua D.; Harvill, Jane L.; Hansen, Clifford W.

2016-03-01

Here, we evaluate semiparametric spatio-temporal models for global horizontal irradiance at high spatial and temporal resolution. These models represent the spatial domain as a lattice and are capable of predicting irradiance at lattice points, given data measured at other lattice points. Using data from a 1.2 MW PV plant located in Lanai, Hawaii, we show that a semiparametric model can be more accurate than simple interpolation between sensor locations. We investigate spatio-temporal models with separable and nonseparable covariance structures and find no evidence to support assuming a separable covariance structure. These results indicate a promising approach for modeling irradiance atmore » high spatial resolution consistent with available ground-based measurements. Moreover, this kind of modeling may find application in design, valuation, and operation of fleets of utility-scale photovoltaic power systems.« less

Optimum Image Formation for Spaceborne Microwave Radiometer Products.

PubMed

Long, David G; Brodzik, Mary J

2016-05-01

This paper considers some of the issues of radiometer brightness image formation and reconstruction for use in the NASA-sponsored Calibrated Passive Microwave Daily Equal-Area Scalable Earth Grid 2.0 Brightness Temperature Earth System Data Record project, which generates a multisensor multidecadal time series of high-resolution radiometer products designed to support climate studies. Two primary reconstruction algorithms are considered: the Backus-Gilbert approach and the radiometer form of the scatterometer image reconstruction (SIR) algorithm. These are compared with the conventional drop-in-the-bucket (DIB) gridded image formation approach. Tradeoff study results for the various algorithm options are presented to select optimum values for the grid resolution, the number of SIR iterations, and the BG gamma parameter. We find that although both approaches are effective in improving the spatial resolution of the surface brightness temperature estimates compared to DIB, SIR requires significantly less computation. The sensitivity of the reconstruction to the accuracy of the measurement spatial response function (MRF) is explored. The partial reconstruction of the methods can tolerate errors in the description of the sensor measurement response function, which simplifies the processing of historic sensor data for which the MRF is not known as well as modern sensors. Simulation tradeoff results are confirmed using actual data.

Estimating Morning Change in Land Surface Temperature from MODIS Day/Night Observations: Applications for Surface Energy Balance Modeling.

PubMed

Hain, Christopher R; Anderson, Martha C

2017-10-16

Observations of land surface temperature (LST) are crucial for the monitoring of surface energy fluxes from satellite. Methods that require high temporal resolution LST observations (e.g., from geostationary orbit) can be difficult to apply globally because several geostationary sensors are required to attain near-global coverage (60°N to 60°S). While these LST observations are available from polar-orbiting sensors, providing global coverage at higher spatial resolutions, the temporal sampling (twice daily observations) can pose significant limitations. For example, the Atmosphere Land Exchange Inverse (ALEXI) surface energy balance model, used for monitoring evapotranspiration and drought, requires an observation of the morning change in LST - a quantity not directly observable from polar-orbiting sensors. Therefore, we have developed and evaluated a data-mining approach to estimate the mid-morning rise in LST from a single sensor (2 observations per day) of LST from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the Aqua platform. In general, the data-mining approach produced estimates with low relative error (5 to 10%) and statistically significant correlations when compared against geostationary observations. This approach will facilitate global, near real-time applications of ALEXI at higher spatial and temporal coverage from a single sensor than currently achievable with current geostationary datasets.

Facing the phase problem in Coherent Diffractive Imaging via Memetic Algorithms.

PubMed

Colombo, Alessandro; Galli, Davide Emilio; De Caro, Liberato; Scattarella, Francesco; Carlino, Elvio

2017-02-09

Coherent Diffractive Imaging is a lensless technique that allows imaging of matter at a spatial resolution not limited by lens aberrations. This technique exploits the measured diffraction pattern of a coherent beam scattered by periodic and non-periodic objects to retrieve spatial information. The diffracted intensity, for weak-scattering objects, is proportional to the modulus of the Fourier Transform of the object scattering function. Any phase information, needed to retrieve its scattering function, has to be retrieved by means of suitable algorithms. Here we present a new approach, based on a memetic algorithm, i.e. a hybrid genetic algorithm, to face the phase problem, which exploits the synergy of deterministic and stochastic optimization methods. The new approach has been tested on simulated data and applied to the phasing of transmission electron microscopy coherent electron diffraction data of a SrTiO 3 sample. We have been able to quantitatively retrieve the projected atomic potential, and also image the oxygen columns, which are not directly visible in the relevant high-resolution transmission electron microscopy images. Our approach proves to be a new powerful tool for the study of matter at atomic resolution and opens new perspectives in those applications in which effective phase retrieval is necessary.

A multiscale Bayesian data integration approach for mapping air dose rates around the Fukushima Daiichi Nuclear Power Plant.

PubMed

Wainwright, Haruko M; Seki, Akiyuki; Chen, Jinsong; Saito, Kimiaki

2017-02-01

This paper presents a multiscale data integration method to estimate the spatial distribution of air dose rates in the regional scale around the Fukushima Daiichi Nuclear Power Plant. We integrate various types of datasets, such as ground-based walk and car surveys, and airborne surveys, all of which have different scales, resolutions, spatial coverage, and accuracy. This method is based on geostatistics to represent spatial heterogeneous structures, and also on Bayesian hierarchical models to integrate multiscale, multi-type datasets in a consistent manner. The Bayesian method allows us to quantify the uncertainty in the estimates, and to provide the confidence intervals that are critical for robust decision-making. Although this approach is primarily data-driven, it has great flexibility to include mechanistic models for representing radiation transport or other complex correlations. We demonstrate our approach using three types of datasets collected at the same time over Fukushima City in Japan: (1) coarse-resolution airborne surveys covering the entire area, (2) car surveys along major roads, and (3) walk surveys in multiple neighborhoods. Results show that the method can successfully integrate three types of datasets and create an integrated map (including the confidence intervals) of air dose rates over the domain in high resolution. Moreover, this study provides us with various insights into the characteristics of each dataset, as well as radiocaesium distribution. In particular, the urban areas show high heterogeneity in the contaminant distribution due to human activities as well as large discrepancy among different surveys due to such heterogeneity. Copyright © 2016 Elsevier Ltd. All rights reserved.

Three dimensional time reversal optical tomography

NASA Astrophysics Data System (ADS)

Wu, Binlin; Cai, W.; Alrubaiee, M.; Xu, M.; Gayen, S. K.

2011-03-01

Time reversal optical tomography (TROT) approach is used to detect and locate absorptive targets embedded in a highly scattering turbid medium to assess its potential in breast cancer detection. TROT experimental arrangement uses multi-source probing and multi-detector signal acquisition and Multiple-Signal-Classification (MUSIC) algorithm for target location retrieval. Light transport from multiple sources through the intervening medium with embedded targets to the detectors is represented by a response matrix constructed using experimental data. A TR matrix is formed by multiplying the response matrix by its transpose. The eigenvectors with leading non-zero eigenvalues of the TR matrix correspond to embedded objects. The approach was used to: (a) obtain the location and spatial resolution of an absorptive target as a function of its axial position between the source and detector planes; and (b) study variation in spatial resolution of two targets at the same axial position but different lateral positions. The target(s) were glass sphere(s) of diameter ~9 mm filled with ink (absorber) embedded in a 60 mm-thick slab of Intralipid-20% suspension in water with an absorption coefficient μa ~ 0.003 mm-1 and a transport mean free path lt ~ 1 mm at 790 nm, which emulate the average values of those parameters for human breast tissue. The spatial resolution and accuracy of target location depended on axial position, and target contrast relative to the background. Both the targets could be resolved and located even when they were only 4-mm apart. The TROT approach is fast, accurate, and has the potential to be useful in breast cancer detection and localization.

Processing the image gradient field using a topographic primal sketch approach.

PubMed

Gambaruto, A M

2015-03-01

The spatial derivatives of the image intensity provide topographic information that may be used to identify and segment objects. The accurate computation of the derivatives is often hampered in medical images by the presence of noise and a limited resolution. This paper focuses on accurate computation of spatial derivatives and their subsequent use to process an image gradient field directly, from which an image with improved characteristics can be reconstructed. The improvements include noise reduction, contrast enhancement, thinning object contours and the preservation of edges. Processing the gradient field directly instead of the image is shown to have numerous benefits. The approach is developed such that the steps are modular, allowing the overall method to be improved and possibly tailored to different applications. As presented, the approach relies on a topographic representation and primal sketch of an image. Comparisons with existing image processing methods on a synthetic image and different medical images show improved results and accuracy in segmentation. Here, the focus is on objects with low spatial resolution, which is often the case in medical images. The methods developed show the importance of improved accuracy in derivative calculation and the potential in processing the image gradient field directly. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Generating Ground Reference Data for a Global Impervious Surface Survey

NASA Technical Reports Server (NTRS)

Tilton, James C.; deColstoun, Eric Brown; Wolfe, Robert E.; Tan, Bin; Huang, Chengquan

2012-01-01

We are engaged in a project to produce a 30m impervious cover data set of the entire Earth for the years 2000 and 2010 based on the Landsat Global Land Survey (GLS) data set. The GLS data from Landsat provide an unprecedented opportunity to map global urbanization at this resolution for the first time, with unprecedented detail and accuracy. Moreover, the spatial resolution of Landsat is absolutely essential to accurately resolve urban targets such as buildings, roads and parking lots. Finally, with GLS data available for the 1975, 1990, 2000, and 2005 time periods, and soon for the 2010 period, the land cover/use changes due to urbanization can now be quantified at this spatial scale as well. Our approach works across spatial scales using very high spatial resolution commercial satellite data to both produce and evaluate continental scale products at the 30m spatial resolution of Landsat data. We are developing continental scale training data at 1m or so resolution and aggregating these to 30m for training a regression tree algorithm. Because the quality of the input training data are critical, we have developed an interactive software tool, called HSegLearn, to facilitate the photo-interpretation of high resolution imagery data, such as Quickbird or Ikonos data, into an impervious versus non-impervious map. Previous work has shown that photo-interpretation of high resolution data at 1 meter resolution will generate an accurate 30m resolution ground reference when coarsened to that resolution. Since this process can be very time consuming when using standard clustering classification algorithms, we are looking at image segmentation as a potential avenue to not only improve the training process but also provide a semi-automated approach for generating the ground reference data. HSegLearn takes as its input a hierarchical set of image segmentations produced by the HSeg image segmentation program [1, 2]. HSegLearn lets an analyst specify pixel locations as being either positive or negative examples, and displays a classification of the study area based on these examples. For our study, the positive examples are examples of impervious surfaces and negative examples are examples of non-impervious surfaces. HSegLearn searches the hierarchical segmentation from HSeg for the coarsest level of segmentation at which selected positive example locations do not conflict with negative example locations and labels the image accordingly. The negative example regions are always defined at the finest level of segmentation detail. The resulting classification map can be then further edited at a region object level using the previously developed HSegViewer tool [3]. After providing an overview of the HSeg image segmentation program, we provide a detailed description of the HSegLearn software tool. We then give examples of using HSegLearn to generate ground reference data and conclude with comments on the effectiveness of the HSegLearn tool.

A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification

NASA Astrophysics Data System (ADS)

Zhang, Ce; Pan, Xin; Li, Huapeng; Gardiner, Andy; Sargent, Isabel; Hare, Jonathon; Atkinson, Peter M.

2018-06-01

The contextual-based convolutional neural network (CNN) with deep architecture and pixel-based multilayer perceptron (MLP) with shallow structure are well-recognized neural network algorithms, representing the state-of-the-art deep learning method and the classical non-parametric machine learning approach, respectively. The two algorithms, which have very different behaviours, were integrated in a concise and effective way using a rule-based decision fusion approach for the classification of very fine spatial resolution (VFSR) remotely sensed imagery. The decision fusion rules, designed primarily based on the classification confidence of the CNN, reflect the generally complementary patterns of the individual classifiers. In consequence, the proposed ensemble classifier MLP-CNN harvests the complementary results acquired from the CNN based on deep spatial feature representation and from the MLP based on spectral discrimination. Meanwhile, limitations of the CNN due to the adoption of convolutional filters such as the uncertainty in object boundary partition and loss of useful fine spatial resolution detail were compensated. The effectiveness of the ensemble MLP-CNN classifier was tested in both urban and rural areas using aerial photography together with an additional satellite sensor dataset. The MLP-CNN classifier achieved promising performance, consistently outperforming the pixel-based MLP, spectral and textural-based MLP, and the contextual-based CNN in terms of classification accuracy. This research paves the way to effectively address the complicated problem of VFSR image classification.

A new approach to large area microchannel plate manufacture

NASA Technical Reports Server (NTRS)

1986-01-01

Methods of manufacture of twisted single elements as the base for producing microchannel plates (MCP) are discussed. Initial evaluations validated the off-axis channel concept and no technological roadblocks were identified which would prevent fabrication of high gain, high spatial resolution, large format MCP's using this technique. The first MP's have operated at stable gains of 3 million with pulse height resolution superior to results obtained by standard chevron MCP's.

JPRS Report, Science and Technology, Europe.

DTIC Science & Technology

1989-06-16

nature of their central energy sources, to the complex distribution of gases around the nucleus and possibly to understanding the origin of the diffuse...development. CCD’s are ideal for single photon X-ray imaging and spectroscopy. They have a high quantum efficiency over a broad energy range, high spatial...resolution, low readout noise, and an energy resolution approaching 100 at high energy levels. Reflection gratings have been chosen for XMM rather

A review of surface energy balance models for estimating actual evapotranspiration with remote sensing at high spatiotemporal resolution over large extents

Treesearch

Ryan R. McShane; Katelyn P. Driscoll; Roy Sando

2017-01-01

Many approaches have been developed for measuring or estimating actual evapotranspiration (ETa), and research over many years has led to the development of remote sensing methods that are reliably reproducible and effective in estimating ETa. Several remote sensing methods can be used to estimate ETa at the high spatial resolution of agricultural fields and the large...

Adaptive windowing in contrast-enhanced intravascular ultrasound imaging

PubMed Central

Lindsey, Brooks D.; Martin, K. Heath; Jiang, Xiaoning; Dayton, Paul A.

2016-01-01

Intravascular ultrasound (IVUS) is one of the most commonly-used interventional imaging techniques and has seen recent innovations which attempt to characterize the risk posed by atherosclerotic plaques. One such development is the use of microbubble contrast agents to image vasa vasorum, fine vessels which supply oxygen and nutrients to the walls of coronary arteries and typically have diameters less than 200 µm. The degree of vasa vasorum neovascularization within plaques is positively correlated with plaque vulnerability. Having recently presented a prototype dual-frequency transducer for contrast agent-specific intravascular imaging, here we describe signal processing approaches based on minimum variance (MV) beamforming and the phase coherence factor (PCF) for improving the spatial resolution and contrast-to-tissue ratio (CTR) in IVUS imaging. These approaches are examined through simulations, phantom studies, ex vivo studies in porcine arteries, and in vivo studies in chicken embryos. In phantom studies, PCF processing improved CTR by a mean of 4.2 dB, while combined MV and PCF processing improved spatial resolution by 41.7%. Improvements of 2.2 dB in CTR and 37.2% in resolution were observed in vivo. Applying these processing strategies can enhance image quality in conventional B-mode IVUS or in contrast-enhanced IVUS, where signal-to-noise ratio is relatively low and resolution is at a premium. PMID:27161022

Regional-scale integration of hydrological and geophysical data using Bayesian sequential simulation: application to field data

NASA Astrophysics Data System (ADS)

Ruggeri, Paolo; Irving, James; Gloaguen, Erwan; Holliger, Klaus

2013-04-01

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending corresponding approaches to the regional scale still represents a major challenge, yet is critically important for the development of groundwater flow and contaminant transport models. To address this issue, we have developed a regional-scale hydrogeophysical data integration technique based on a two-step Bayesian sequential simulation procedure. The objective is to simulate the regional-scale distribution of a hydraulic parameter based on spatially exhaustive, but poorly resolved, measurements of a pertinent geophysical parameter and locally highly resolved, but spatially sparse, measurements of the considered geophysical and hydraulic parameters. To this end, our approach first involves linking the low- and high-resolution geophysical data via a downscaling procedure before relating the downscaled regional-scale geophysical data to the high-resolution hydraulic parameter field. We present the application of this methodology to a pertinent field scenario, where we consider collocated high-resolution measurements of the electrical conductivity, measured using a cone penetrometer testing (CPT) system, and the hydraulic conductivity, estimated from EM flowmeter and slug test measurements, in combination with low-resolution exhaustive electrical conductivity estimates obtained from dipole-dipole ERT meausurements.

Portable lensless wide-field microscopy imaging platform based on digital inline holography and multi-frame pixel super-resolution

PubMed Central

Sobieranski, Antonio C; Inci, Fatih; Tekin, H Cumhur; Yuksekkaya, Mehmet; Comunello, Eros; Cobra, Daniel; von Wangenheim, Aldo; Demirci, Utkan

2017-01-01

In this paper, an irregular displacement-based lensless wide-field microscopy imaging platform is presented by combining digital in-line holography and computational pixel super-resolution using multi-frame processing. The samples are illuminated by a nearly coherent illumination system, where the hologram shadows are projected into a complementary metal-oxide semiconductor-based imaging sensor. To increase the resolution, a multi-frame pixel resolution approach is employed to produce a single holographic image from multiple frame observations of the scene, with small planar displacements. Displacements are resolved by a hybrid approach: (i) alignment of the LR images by a fast feature-based registration method, and (ii) fine adjustment of the sub-pixel information using a continuous optimization approach designed to find the global optimum solution. Numerical method for phase-retrieval is applied to decode the signal and reconstruct the morphological details of the analyzed sample. The presented approach was evaluated with various biological samples including sperm and platelets, whose dimensions are in the order of a few microns. The obtained results demonstrate a spatial resolution of 1.55 µm on a field-of-view of ≈30 mm2. PMID:29657866

Towards the Optimal Pixel Size of dem for Automatic Mapping of Landslide Areas

NASA Astrophysics Data System (ADS)

Pawłuszek, K.; Borkowski, A.; Tarolli, P.

2017-05-01

Determining appropriate spatial resolution of digital elevation model (DEM) is a key step for effective landslide analysis based on remote sensing data. Several studies demonstrated that choosing the finest DEM resolution is not always the best solution. Various DEM resolutions can be applicable for diverse landslide applications. Thus, this study aims to assess the influence of special resolution on automatic landslide mapping. Pixel-based approach using parametric and non-parametric classification methods, namely feed forward neural network (FFNN) and maximum likelihood classification (ML), were applied in this study. Additionally, this allowed to determine the impact of used classification method for selection of DEM resolution. Landslide affected areas were mapped based on four DEMs generated at 1 m, 2 m, 5 m and 10 m spatial resolution from airborne laser scanning (ALS) data. The performance of the landslide mapping was then evaluated by applying landslide inventory map and computation of confusion matrix. The results of this study suggests that the finest scale of DEM is not always the best fit, however working at 1 m DEM resolution on micro-topography scale, can show different results. The best performance was found at 5 m DEM-resolution for FFNN and 1 m DEM resolution for results. The best performance was found to be using 5 m DEM-resolution for FFNN and 1 m DEM resolution for ML classification.

Object Based Image Analysis Combining High Spatial Resolution Imagery and Laser Point Clouds for Urban Land Cover

NASA Astrophysics Data System (ADS)

Zou, Xiaoliang; Zhao, Guihua; Li, Jonathan; Yang, Yuanxi; Fang, Yong

2016-06-01

With the rapid developments of the sensor technology, high spatial resolution imagery and airborne Lidar point clouds can be captured nowadays, which make classification, extraction, evaluation and analysis of a broad range of object features available. High resolution imagery, Lidar dataset and parcel map can be widely used for classification as information carriers. Therefore, refinement of objects classification is made possible for the urban land cover. The paper presents an approach to object based image analysis (OBIA) combing high spatial resolution imagery and airborne Lidar point clouds. The advanced workflow for urban land cover is designed with four components. Firstly, colour-infrared TrueOrtho photo and laser point clouds were pre-processed to derive the parcel map of water bodies and nDSM respectively. Secondly, image objects are created via multi-resolution image segmentation integrating scale parameter, the colour and shape properties with compactness criterion. Image can be subdivided into separate object regions. Thirdly, image objects classification is performed on the basis of segmentation and a rule set of knowledge decision tree. These objects imagery are classified into six classes such as water bodies, low vegetation/grass, tree, low building, high building and road. Finally, in order to assess the validity of the classification results for six classes, accuracy assessment is performed through comparing randomly distributed reference points of TrueOrtho imagery with the classification results, forming the confusion matrix and calculating overall accuracy and Kappa coefficient. The study area focuses on test site Vaihingen/Enz and a patch of test datasets comes from the benchmark of ISPRS WG III/4 test project. The classification results show higher overall accuracy for most types of urban land cover. Overall accuracy is 89.5% and Kappa coefficient equals to 0.865. The OBIA approach provides an effective and convenient way to combine high resolution imagery and Lidar ancillary data for classification of urban land cover.

Preliminary validation of WRF model in two Arctic fjords, Hornsund and Porsanger

NASA Astrophysics Data System (ADS)

Aniskiewicz, Paulina; Stramska, Małgorzata

2017-04-01

Our research is focused on development of efficient modeling system for arctic fjords. This tool should include high-resolution meteorological data derived using downscaling approach. In this presentation we have focused on modeling, with high spatial resolution, of the meteorological conditions in two Arctic fjords: Hornsund (H), located in the western part of Svalbard archipelago and Porsanger (P) located in the coastal waters of the Barents Sea. The atmospheric downscaling is based on The Weather Research and Forecasting Model (WRF, www.wrf-model.org) with polar stereographic projection. We have created two parent domains with grid point distances of about 3.2 km (P) and 3.0 km (H) and with nested domains (almost 5 times higher resolution than parent domains). We tested what is the impact of the spatial resolution of the model on derived meteorological quantities. For both fjords the input topography data resolution is 30 sec. To validate the results we have used meteorological data from the Norwegian Meteorological Institute for stations Lakselv (L) and Honningsvåg (Ho) located in the inner and outer parts of the Porsanger fjord as well as from station in the outer part of the Hornsund fjord. We have estimated coefficients of determination (r2), statistical errors (St) and systematic errors (Sy) between measured and modelled air temperature and wind speed at each station. This approach will allow us to create high resolution spatially variable meteorological fields that will serve as forcing for numerical models of the fjords. We will investigate the role of different meteorological quantities (e. g. wind, solar insolation, precipitation) on hydrohraphic processes in fjords. The project has been financed from the funds of the Leading National Research Centre (KNOW) received by the Centre for Polar Studies for the period 2014-2018. This work was also funded by the Norway Grants (NCBR contract No. 201985, project NORDFLUX). Partial support comes from the Institute of Oceanology (IO PAN).

A global approach to estimate irrigated areas - a comparison between different data and statistics

NASA Astrophysics Data System (ADS)

Meier, Jonas; Zabel, Florian; Mauser, Wolfram

2018-02-01

Agriculture is the largest global consumer of water. Irrigated areas constitute 40 % of the total area used for agricultural production (FAO, 2014a) Information on their spatial distribution is highly relevant for regional water management and food security. Spatial information on irrigation is highly important for policy and decision makers, who are facing the transition towards more efficient sustainable agriculture. However, the mapping of irrigated areas still represents a challenge for land use classifications, and existing global data sets differ strongly in their results. The following study tests an existing irrigation map based on statistics and extends the irrigated area using ancillary data. The approach processes and analyzes multi-temporal normalized difference vegetation index (NDVI) SPOT-VGT data and agricultural suitability data - both at a spatial resolution of 30 arcsec - incrementally in a multiple decision tree. It covers the period from 1999 to 2012. The results globally show a 18 % larger irrigated area than existing approaches based on statistical data. The largest differences compared to the official national statistics are found in Asia and particularly in China and India. The additional areas are mainly identified within already known irrigated regions where irrigation is more dense than previously estimated. The validation with global and regional products shows the large divergence of existing data sets with respect to size and distribution of irrigated areas caused by spatial resolution, the considered time period and the input data and assumption made.

Non-Invasive Measurement of Frog Skin Reflectivity in High Spatial Resolution Using a Dual Hyperspectral Approach

PubMed Central

Liebisch, Frank; Walter, Achim; Greven, Hartmut; Rascher, Uwe

2013-01-01

Background Most spectral data for the amphibian integument are limited to the visible spectrum of light and have been collected using point measurements with low spatial resolution. In the present study a dual camera setup consisting of two push broom hyperspectral imaging systems was employed, which produces reflectance images between 400 and 2500 nm with high spectral and spatial resolution and a high dynamic range. Methodology/Principal Findings We briefly introduce the system and document the high efficiency of this technique analyzing exemplarily the spectral reflectivity of the integument of three arboreal anuran species (Litoria caerulea, Agalychnis callidryas and Hyla arborea), all of which appear green to the human eye. The imaging setup generates a high number of spectral bands within seconds and allows non-invasive characterization of spectral characteristics with relatively high working distance. Despite the comparatively uniform coloration, spectral reflectivity between 700 and 1100 nm differed markedly among the species. In contrast to H. arborea, L. caerulea and A. callidryas showed reflection in this range. For all three species, reflectivity above 1100 nm is primarily defined by water absorption. Furthermore, the high resolution allowed examining even small structures such as fingers and toes, which in A. callidryas showed an increased reflectivity in the near infrared part of the spectrum. Conclusion/Significance Hyperspectral imaging was found to be a very useful alternative technique combining the spectral resolution of spectrometric measurements with a higher spatial resolution. In addition, we used Digital Infrared/Red-Edge Photography as new simple method to roughly determine the near infrared reflectivity of frog specimens in field, where hyperspectral imaging is typically difficult. PMID:24058464

Deriving temporally continuous soil moisture estimations at fine resolution by downscaling remotely sensed product

NASA Astrophysics Data System (ADS)

Jin, Yan; Ge, Yong; Wang, Jianghao; Heuvelink, Gerard B. M.

2018-06-01

Land surface soil moisture (SSM) has important roles in the energy balance of the land surface and in the water cycle. Downscaling of coarse-resolution SSM remote sensing products is an efficient way for producing fine-resolution data. However, the downscaling methods used most widely require full-coverage visible/infrared satellite data as ancillary information. These methods are restricted to cloud-free days, making them unsuitable for continuous monitoring. The purpose of this study is to overcome this limitation to obtain temporally continuous fine-resolution SSM estimations. The local spatial heterogeneities of SSM and multiscale ancillary variables were considered in the downscaling process both to solve the problem of the strong variability of SSM and to benefit from the fusion of ancillary information. The generation of continuous downscaled remote sensing data was achieved via two principal steps. For cloud-free days, a stepwise hybrid geostatistical downscaling approach, based on geographically weighted area-to-area regression kriging (GWATARK), was employed by combining multiscale ancillary variables with passive microwave remote sensing data. Then, the GWATARK-estimated SSM and China Soil Moisture Dataset from Microwave Data Assimilation SSM data were combined to estimate fine-resolution data for cloudy days. The developed methodology was validated by application to the 25-km resolution daily AMSR-E SSM product to produce continuous SSM estimations at 1-km resolution over the Tibetan Plateau. In comparison with ground-based observations, the downscaled estimations showed correlation (R ≥ 0.7) for both ascending and descending overpasses. The analysis indicated the high potential of the proposed approach for producing a temporally continuous SSM product at fine spatial resolution.

Characterizing the Diurnal Cycle of Land Surface Temperature and Evapotranspiration at High Spatial Resolution Using Thermal Observations from sUAS.

NASA Astrophysics Data System (ADS)

Dutta, D.; Drewry, D.; Johnson, W. R.

2017-12-01

The surface temperature of plant canopies is an important indicator of the stomatal regulation of plant water use and the associated water flux from plants to atmosphere (evapotranspiration (ET)). Remotely sensed thermal observations using compact, low-cost, lightweight sensors from small unmanned aerial systems (sUAS) have the potential to provide surface temperature (ST) and ET estimates at unprecedented spatial and temporal resolutions, allowing us to characterize the intra-field diurnal variations in canopy ST and ET for a variety of vegetation systems. However, major challenges exist for obtaining accurate surface temperature estimates from low-cost uncooled microbolometer-type sensors. Here we describe the development of calibration methods using thermal chamber experiments, taking into account the ambient optics and sensor temperatures, and applying simple models of spatial non-uniformity correction to the sensor focal-plane-array. We present a framework that can be used to derive accurate surface temperatures using radiometric observations from low-cost sensors, and demonstrate this framework using a sUAS-mounted sensor across a diverse set of calibration and vegetation targets. Further, we demonstrate the use of the Surface Temperature Initiated Closure (STIC) model for computing spatially explicit, high spatial resolution ET estimates across several well-monitored agricultural systems, as driven by sUAS acquired surface temperatures. STIC provides a physically-based surface energy balance framework for the simultaneous retrieval of the surface and atmospheric vapor conductances and surface energy fluxes, by physically integrating radiometric surface temperature information into the Penman-Monteith equation. Results of our analysis over agricultural systems in Ames, IA and Davis, CA demonstrate the power of this approach for quantifying the intra-field spatial variability in the diurnal cycle of plant water use at sub-meter resolutions.

MTF analysis of LANDSAT-4 Thematic Mapper

NASA Technical Reports Server (NTRS)

Schowengerdt, R.

1983-01-01

The spatial radiance distribution of a ground target must be known to a resolution at least four to five times greater than that of the system under test when measuring a satellite sensor's modulation transfer function. Calibration of the target requires either the use of man-made special purpose targets with known properties, e.g., a small reflective mirror or a dark-light linear pattern such as line or edge, or use of relatively high resolution underflight imagery to calibrate an arbitrary ground scene. Both approaches are to be used in addition a technique that utilizes an analytical model for the scene spatial frequency power spectrum is being investigated as an alternative to calibration of the scene.

MTF Analysis of LANDSAT-4 Thematic Mapper

NASA Technical Reports Server (NTRS)

Schowengerdt, R.

1985-01-01

The spatial radiance distribution of a ground target must be known to a resolution at least four to five times greater than that of the system under test when measuring a satellite sensor's modulation transfer function. Calibration of the target requires either the use of man-made special purpose targets with known properties, e.g., a small reflective mirror or a dark-light linear pattern such as line or edge, or use of relatively high resolution underflight imagery to calibrate an arbitrary ground scene. Both approaches are to be used, in addition a technique that utilizes an analytical model of the scene spatial frequency power spectrum is being investigated as an alternative to calibration of the scene.

Comparison of Focused and Near-Field Imaging of Spray on Foam Insulation (SOFI) at Millimeter Wave Frequencies

NASA Technical Reports Server (NTRS)

Kharkovshy, S.; Zoughi, R.; Hepburn, F. L.

2007-01-01

Millimeter wave imaging techniques can provide high spatial-resolution images of various composites. Lens antennas may be incorporated into the imaging system to provide a small incident beam footprint. Another approach may involve the use of horn antennas, which if operating in their near-fields, images with reasonably high spatial-resolutions may also be obtained. This paper gives a comparison between such near-field and focused far-field imaging of the Space Shuttle Spray on Foam Insulation (SOFI) used in its external fuel tank at millimeter wave frequencies. Small horn antennas and lens antennas with relatively long depth of focus were used in this investigation.

Fluorescence lifetime microscopy with a time- and space-resolved single-photon counting detector

PubMed Central

Michalet, X.; Siegmund, O.H.W.; Vallerga, J.V.; Jelinsky, P.; Pinaud, F. F.; Millaud, J.E.; Weiss, S.

2017-01-01

We have recently developed a wide-field photon-counting detector (the H33D detector) having high-temporal and high-spatial resolutions and capable of recording up to 500,000 photons per sec. Its temporal performance has been previously characterized using solutions of fluorescent materials with different lifetimes, and its spatial resolution using sub-diffraction objects (beads and quantum dots). Here we show its application to fluorescence lifetime imaging of live cells and compare its performance to a scanning confocal TCSPC approach. With the expected improvements in photocathode sensitivity and increase in detector throughput, this technology appears as a promising alternative to the current lifetime imaging solutions. PMID:29449756

Extended-range high-resolution dynamical downscaling over a continental-scale spatial domain with atmospheric and surface nudging

NASA Astrophysics Data System (ADS)

Husain, S. Z.; Separovic, L.; Yu, W.; Fernig, D.

2014-12-01

Extended-range high-resolution mesoscale simulations with limited-area atmospheric models when applied to downscale regional analysis fields over large spatial domains can provide valuable information for many applications including the weather-dependent renewable energy industry. Long-term simulations over a continental-scale spatial domain, however, require mechanisms to control the large-scale deviations in the high-resolution simulated fields from the coarse-resolution driving fields. As enforcement of the lateral boundary conditions is insufficient to restrict such deviations, large scales in the simulated high-resolution meteorological fields are therefore spectrally nudged toward the driving fields. Different spectral nudging approaches, including the appropriate nudging length scales as well as the vertical profiles and temporal relaxations for nudging, have been investigated to propose an optimal nudging strategy. Impacts of time-varying nudging and generation of hourly analysis estimates are explored to circumvent problems arising from the coarse temporal resolution of the regional analysis fields. Although controlling the evolution of the atmospheric large scales generally improves the outputs of high-resolution mesoscale simulations within the surface layer, the prognostically evolving surface fields can nevertheless deviate from their expected values leading to significant inaccuracies in the predicted surface layer meteorology. A forcing strategy based on grid nudging of the different surface fields, including surface temperature, soil moisture, and snow conditions, toward their expected values obtained from a high-resolution offline surface scheme is therefore proposed to limit any considerable deviation. Finally, wind speed and temperature at wind turbine hub height predicted by different spectrally nudged extended-range simulations are compared against observations to demonstrate possible improvements achievable using higher spatiotemporal resolution.

Estimation of improved resolution soil moisture in vegetated areas using passive AMSR-E data

NASA Astrophysics Data System (ADS)

Moradizadeh, Mina; Saradjian, Mohammad R.

2018-03-01

Microwave remote sensing provides a unique capability for soil parameter retrievals. Therefore, various soil parameters estimation models have been developed using brightness temperature (BT) measured by passive microwave sensors. Due to the low resolution of satellite microwave radiometer data, the main goal of this study is to develop a downscaling approach to improve the spatial resolution of soil moisture estimates with the use of higher resolution visible/infrared sensor data. Accordingly, after the soil parameters have been obtained using Simultaneous Land Parameters Retrieval Model algorithm, the downscaling method has been applied to the soil moisture estimations that have been validated against in situ soil moisture data. Advance Microwave Scanning Radiometer-EOS BT data in Soil Moisture Experiment 2003 region in the south and north of Oklahoma have been used to this end. Results illustrated that the soil moisture variability is effectively captured at 5 km spatial scales without a significant degradation of the accuracy.

Spatio-temporal modelling for assessing air pollution in Santiago de Chile

NASA Astrophysics Data System (ADS)

Nicolis, Orietta; Camaño, Christian; Mařın, Julio C.; Sahu, Sujit K.

2017-01-01

In this work, we propose a space-time approach for studying the PM2.5 concentration in the city of Santiago de Chile. In particular, we apply the autoregressive hierarchical model proposed by [1] using the PM2.5 observations collected by a monitoring network as a response variable and numerical weather forecasts from the Weather Research and Forecasting (WRF) model as covariate together with spatial and temporal (periodic) components. The approach is able to provide short-term spatio-temporal predictions of PM2.5 concentrations on a fine spatial grid (at 1km × 1km horizontal resolution.)

Visual brain activity patterns classification with simultaneous EEG-fMRI: A multimodal approach.

PubMed

Ahmad, Rana Fayyaz; Malik, Aamir Saeed; Kamel, Nidal; Reza, Faruque; Amin, Hafeez Ullah; Hussain, Muhammad

2017-01-01

Classification of the visual information from the brain activity data is a challenging task. Many studies reported in the literature are based on the brain activity patterns using either fMRI or EEG/MEG only. EEG and fMRI considered as two complementary neuroimaging modalities in terms of their temporal and spatial resolution to map the brain activity. For getting a high spatial and temporal resolution of the brain at the same time, simultaneous EEG-fMRI seems to be fruitful. In this article, we propose a new method based on simultaneous EEG-fMRI data and machine learning approach to classify the visual brain activity patterns. We acquired EEG-fMRI data simultaneously on the ten healthy human participants by showing them visual stimuli. Data fusion approach is used to merge EEG and fMRI data. Machine learning classifier is used for the classification purposes. Results showed that superior classification performance has been achieved with simultaneous EEG-fMRI data as compared to the EEG and fMRI data standalone. This shows that multimodal approach improved the classification accuracy results as compared with other approaches reported in the literature. The proposed simultaneous EEG-fMRI approach for classifying the brain activity patterns can be helpful to predict or fully decode the brain activity patterns.

Impact of temporal upscaling and chemical transport model horizontal resolution on reducing ozone exposure misclassification

NASA Astrophysics Data System (ADS)

Xu, Yadong; Serre, Marc L.; Reyes, Jeanette M.; Vizuete, William

2017-10-01

We have developed a Bayesian Maximum Entropy (BME) framework that integrates observations from a surface monitoring network and predictions from a Chemical Transport Model (CTM) to create improved exposure estimates that can be resolved into any spatial and temporal resolution. The flexibility of the framework allows for input of data in any choice of time scales and CTM predictions of any spatial resolution with varying associated degrees of estimation error and cost in terms of implementation and computation. This study quantifies the impact on exposure estimation error due to these choices by first comparing estimations errors when BME relied on ozone concentration data either as an hourly average, the daily maximum 8-h average (DM8A), or the daily 24-h average (D24A). Our analysis found that the use of DM8A and D24A data, although less computationally intensive, reduced estimation error more when compared to the use of hourly data. This was primarily due to the poorer CTM model performance in the hourly average predicted ozone. Our second analysis compared spatial variability and estimation errors when BME relied on CTM predictions with a grid cell resolution of 12 × 12 km2 versus a coarser resolution of 36 × 36 km2. Our analysis found that integrating the finer grid resolution CTM predictions not only reduced estimation error, but also increased the spatial variability in daily ozone estimates by 5 times. This improvement was due to the improved spatial gradients and model performance found in the finer resolved CTM simulation. The integration of observational and model predictions that is permitted in a BME framework continues to be a powerful approach for improving exposure estimates of ambient air pollution. The results of this analysis demonstrate the importance of also understanding model performance variability and its implications on exposure error.

Adaptive hyperspectral imager: design, modeling, and control

NASA Astrophysics Data System (ADS)

McGregor, Scot; Lacroix, Simon; Monmayrant, Antoine

2015-08-01

An adaptive, hyperspectral imager is presented. We propose a system with easily adaptable spectral resolution, adjustable acquisition time, and high spatial resolution which is independent of spectral resolution. The system yields the possibility to define a variety of acquisition schemes, and in particular near snapshot acquisitions that may be used to measure the spectral content of given or automatically detected regions of interest. The proposed system is modelled and simulated, and tests on a first prototype validate the approach to achieve near snapshot spectral acquisitions without resorting to any computationally heavy post-processing, nor cumbersome calibration

Hyperspectral classification of grassland species: towards a UAS application for semi-automatic field surveys

NASA Astrophysics Data System (ADS)

Lopatin, Javier; Fassnacht, Fabian E.; Kattenborn, Teja; Schmidtlein, Sebastian

2017-04-01

Grasslands are one of the ecosystems that have been strongly intervened during the past decades due to anthropogenic impacts, affecting their structural and functional composition. To monitor the spatial and/or temporal changes of these environments, a reliable field survey is first needed. As quality relevés are usually expensive and time consuming, the amount of information available is usually poor or not well spatially distributed at the regional scale. In the present study, we investigate the possibility of a semi-automated method used for repeated surveys of monitoring sites. We analyze the applicability of very high spatial resolution hyperspectral data to classify grassland species at the level of individuals. The AISA+ imaging spectrometer mounted on a scaffold was applied to scan 1 m2 grassland plots and assess the impact of four sources of variation on the predicted species cover: (1) the spatial resolution of the scans, (2) the species number and structural diversity, (3) the species cover, and (4) the species functional types (bryophytes, forbs and graminoids). We found that the spatial resolution and the diversity level (mainly structural diversity) were the most important source of variation for the proposed approach. A spatial resolution below 1 cm produced relatively high model performances, while predictions with pixel sizes over that threshold produced non adequate results. Areas with low interspecies overlap reached classification median values of 0.8 (kappa). On the contrary, results were not satisfactory in plots with frequent interspecies overlap in multiple layers. By means of a bootstrapping procedure, we found that areas with shadows and mixed pixels introduce uncertainties into the classification. We conclude that the application of very high resolution hyperspectral remote sensing as a robust alternative or supplement to field surveys is possible for environments with low structural heterogeneity. This study presents the first try of a full classification of grassland species at the individuum level using spectral data.

Implementation of marine spatial planning in shellfish aquaculture management: modeling studies in a Norwegian fjord.

PubMed

Filgueira, Ramon; Grant, Jon; Strand, Øivind

2014-06-01

Shellfish carrying capacity is determined by the interaction of a cultured species with its ecosystem, which is strongly influenced by hydrodynamics. Water circulation controls the exchange of matter between farms and the adjacent areas, which in turn establishes the nutrient supply that supports phytoplankton populations. The complexity of water circulation makes necessary the use of hydrodynamic models with detailed spatial resolution in carrying capacity estimations. This detailed spatial resolution also allows for the study of processes that depend on specific spatial arrangements, e.g., the most suitable location to place farms, which is crucial for marine spatial planning, and consequently for decision support systems. In the present study, a fully spatial physical-biogeochemical model has been combined with scenario building and optimization techniques as a proof of concept of the use of ecosystem modeling as an objective tool to inform marine spatial planning. The object of this exercise was to generate objective knowledge based on an ecosystem approach to establish new mussel aquaculture areas in a Norwegian fjord. Scenario building was used to determine the best location of a pump that can be used to bring nutrient-rich deep waters to the euphotic layer, increasing primary production, and consequently, carrying capacity for mussel cultivation. In addition, an optimization tool, parameter estimation (PEST), was applied to the optimal location and mussel standing stock biomass that maximize production, according to a preestablished carrying capacity criterion. Optimization tools allow us to make rational and transparent decisions to solve a well-defined question, decisions that are essential for policy makers. The outcomes of combining ecosystem models with scenario building and optimization facilitate planning based on an ecosystem approach, highlighting the capabilities of ecosystem modeling as a tool for marine spatial planning.

Earthquake Damage Assessment Using Very High Resolution Satelliteimagery

NASA Astrophysics Data System (ADS)

Chiroiu, L.; André, G.; Bahoken, F.; Guillande, R.

Various studies using satellite imagery were applied in the last years in order to assess natural hazard damages, most of them analyzing the case of floods, hurricanes or landslides. For the case of earthquakes, the medium or small spatial resolution data available in the recent past did not allow a reliable identification of damages, due to the size of the elements (e.g. buildings or other structures), too small compared with the pixel size. The recent progresses of remote sensing in terms of spatial resolution and data processing makes possible a reliable damage detection to the elements at risk. Remote sensing techniques applied to IKONOS (1 meter resolution) and IRS (5 meters resolution) imagery were used in order to evaluate seismic vulnerability and post earthquake damages. A fast estimation of losses was performed using a multidisciplinary approach based on earthquake engineering and geospatial analysis. The results, integrated into a GIS database, could be transferred via satellite networks to the rescue teams deployed on the affected zone, in order to better coordinate the emergency operations. The methodology was applied to the city of Bhuj and Anjar after the 2001 Gujarat (India) Earthquake.

Interrogating Surface Functional Group Heterogeneity of Activated Thermoplastics Using Super-Resolution Fluorescence Microscopy.

PubMed

ONeil, Colleen E; Jackson, Joshua M; Shim, Sang-Hee; Soper, Steven A

2016-04-05

We present a novel approach for characterizing surfaces utilizing super-resolution fluorescence microscopy with subdiffraction limit spatial resolution. Thermoplastic surfaces were activated by UV/O3 or O2 plasma treatment under various conditions to generate pendant surface-confined carboxylic acids (-COOH). These surface functional groups were then labeled with a photoswitchable dye and interrogated using single-molecule, localization-based, super-resolution fluorescence microscopy to elucidate the surface heterogeneity of these functional groups across the activated surface. Data indicated nonuniform distributions of these functional groups for both COC and PMMA thermoplastics with the degree of heterogeneity being dose dependent. In addition, COC demonstrated relative higher surface density of functional groups compared to PMMA for both UV/O3 and O2 plasma treatment. The spatial distribution of -COOH groups secured from super-resolution imaging were used to simulate nonuniform patterns of electroosmotic flow in thermoplastic nanochannels. Simulations were compared to single-particle tracking of fluorescent nanoparticles within thermoplastic nanoslits to demonstrate the effects of surface functional group heterogeneity on the electrokinetic transport process.

Assessment of a vertical high-resolution distributed-temperature-sensing system in a shallow thermohaline environment

NASA Astrophysics Data System (ADS)

Suárez, F.; Aravena, J. E.; Hausner, M. B.; Childress, A. E.; Tyler, S. W.

2011-03-01

In shallow thermohaline-driven lakes it is important to measure temperature on fine spatial and temporal scales to detect stratification or different hydrodynamic regimes. Raman spectra distributed temperature sensing (DTS) is an approach available to provide high spatial and temporal temperature resolution. A vertical high-resolution DTS system was constructed to overcome the problems of typical methods used in the past, i.e., without disturbing the water column, and with resistance to corrosive environments. This paper describes a method to quantitatively assess accuracy, precision and other limitations of DTS systems to fully utilize the capacity of this technology, with a focus on vertical high-resolution to measure temperatures in shallow thermohaline environments. It also presents a new method to manually calibrate temperatures along the optical fiber achieving significant improved resolution. The vertical high-resolution DTS system is used to monitor the thermal behavior of a salt-gradient solar pond, which is an engineered shallow thermohaline system that allows collection and storage of solar energy for a long period of time. The vertical high-resolution DTS system monitors the temperature profile each 1.1 cm vertically and in time averages as small as 10 s. Temperature resolution as low as 0.035 °C is obtained when the data are collected at 5-min intervals.

Extracellular oxygen concentration mapping with a confocal multiphoton laser scanning microscope and TCSPC card

NASA Astrophysics Data System (ADS)

Hosny, Neveen A.; Lee, David A.; Knight, Martin M.

2010-02-01

Extracellular oxygen concentrations influence cell metabolism and tissue function. Fluorescence Lifetime Imaging Microscopy (FLIM) offers a non-invasive method for quantifying local oxygen concentrations. However, existing methods show limited spatial resolution and/or require custom made systems. This study describes a new optimised approach for quantitative extracellular oxygen detection, providing an off-the-shelf system with high spatial resolution and an improved lifetime determination over previous techniques, while avoiding systematic photon pile-up. Fluorescence lifetime detection of an oxygen sensitive fluorescent dye, tris(2,2'-bipyridyl)ruthenium(II) chloride hexahydrate [Ru(bipy)3]2+, was measured using a Becker&Hickl time-correlated single photon counting (TCSPC) card with excitation provided by a multi-photon laser. This technique was able to identify a subpopulation of isolated chondrocyte cells, seeded in three-dimensional agarose gel, displaying a significant spatial oxygen gradient. Thus this technique provides a powerful tool for quantifying spatial oxygen gradients within three-dimensional cellular models.

Spatial Resolution Requirements for Traffic-Related Air Pollutant Exposure Evaluations

PubMed Central

Batterman, Stuart; Chambliss, Sarah; Isakov, Vlad

2014-01-01

Vehicle emissions represent one of the most important air pollution sources in most urban areas, and elevated concentrations of pollutants found near major roads have been associated with many adverse health impacts. To understand these impacts, exposure estimates should reflect the spatial and temporal patterns observed for traffic-related air pollutants. This paper evaluates the spatial resolution and zonal systems required to estimate accurately intraurban and near-road exposures of traffic-related air pollutants. The analyses use the detailed information assembled for a large (800 km2) area centered on Detroit, Michigan, USA. Concentrations of nitrogen oxides (NOx) due to vehicle emissions were estimated using hourly traffic volumes and speeds on 9,700 links representing all but minor roads in the city, the MOVES2010 emission model, the RLINE dispersion model, local meteorological data, a temporal resolution of 1 hr, and spatial resolution as low as 10 m. Model estimates were joined with the corresponding shape files to estimate residential exposures for 700,000 individuals at property parcel, census block, census tract, and ZIP code levels. We evaluate joining methods, the spatial resolution needed to meet specific error criteria, and the extent of exposure misclassification. To portray traffic-related air pollutant exposure, raster or inverse distance-weighted interpolations are superior to nearest neighbor approaches, and interpolations between receptors and points of interest should not exceed about 40 m near major roads, and 100 m at larger distances. For census tracts and ZIP codes, average exposures are overestimated since few individuals live very near major roads, the range of concentrations is compressed, most exposures are misclassified, and high concentrations near roads are entirely omitted. While smaller zones improve performance considerably, even block-level data can misclassify many individuals. To estimate exposures and impacts of traffic-related pollutants accurately, data should be geocoded or estimated at the most-resolved spatial level; census tract and larger zones have little if any ability to represent intraurban variation in traffic-related air pollutant concentrations. These results are based on one of the most comprehensive intraurban modeling studies in the literature and results are robust. Recommendations address the value of dispersion models to portray spatial and temporal variation of air pollutants in epidemiology and other studies; techniques to improve accuracy and reduce the computational burden in urban scale modeling; the necessary spatial resolution for health surveillance, demographic, and pollution data; and the consequences of low resolution data in terms of exposure misclassification. PMID:25132794

High Resolution Stratigraphic Mapping in Complex Terrain: A Comparison of Traditional Remote Sensing Techniques with Unmanned Aerial Vehicle - Structure from Motion Photogrammetry

NASA Astrophysics Data System (ADS)

Nesbit, P. R.; Hugenholtz, C.; Durkin, P.; Hubbard, S. M.; Kucharczyk, M.; Barchyn, T.

2016-12-01

Remote sensing and digital mapping have started to revolutionize geologic mapping in recent years as a result of their realized potential to provide high resolution 3D models of outcrops to assist with interpretation, visualization, and obtaining accurate measurements of inaccessible areas. However, in stratigraphic mapping applications in complex terrain, it is difficult to acquire information with sufficient detail at a wide spatial coverage with conventional techniques. We demonstrate the potential of a UAV and Structure from Motion (SfM) photogrammetric approach for improving 3D stratigraphic mapping applications within a complex badland topography. Our case study is performed in Dinosaur Provincial Park (Alberta, Canada), mapping late Cretaceous fluvial meander belt deposits of the Dinosaur Park formation amidst a succession of steeply sloping hills and abundant drainages - creating a challenge for stratigraphic mapping. The UAV-SfM dataset (2 cm spatial resolution) is compared directly with a combined satellite and aerial LiDAR dataset (30 cm spatial resolution) to reveal advantages and limitations of each dataset before presenting a unique workflow that utilizes the dense point cloud from the UAV-SfM dataset for analysis. The UAV-SfM dense point cloud minimizes distortion, preserves 3D structure, and records an RGB attribute - adding potential value in future studies. The proposed UAV-SfM workflow allows for high spatial resolution remote sensing of stratigraphy in complex topographic environments. This extended capability can add value to field observations and has the potential to be integrated with subsurface petroleum models.

Singular value decomposition metrics show limitations of detector design in diffuse fluorescence tomography

PubMed Central

Leblond, Frederic; Tichauer, Kenneth M.; Pogue, Brian W.

2010-01-01

The spatial resolution and recovered contrast of images reconstructed from diffuse fluorescence tomography data are limited by the high scattering properties of light propagation in biological tissue. As a result, the image reconstruction process can be exceedingly vulnerable to inaccurate prior knowledge of tissue optical properties and stochastic noise. In light of these limitations, the optimal source-detector geometry for a fluorescence tomography system is non-trivial, requiring analytical methods to guide design. Analysis of the singular value decomposition of the matrix to be inverted for image reconstruction is one potential approach, providing key quantitative metrics, such as singular image mode spatial resolution and singular data mode frequency as a function of singular mode. In the present study, these metrics are used to analyze the effects of different sources of noise and model errors as related to image quality in the form of spatial resolution and contrast recovery. The image quality is demonstrated to be inherently noise-limited even when detection geometries were increased in complexity to allow maximal tissue sampling, suggesting that detection noise characteristics outweigh detection geometry for achieving optimal reconstructions. PMID:21258566

High-Resolution Mapping of Thermal History in Polymer Nanocomposites: Gold Nanorods as Microscale Temperature Sensors.

PubMed

Kennedy, W Joshua; Slinker, Keith A; Volk, Brent L; Koerner, Hilmar; Godar, Trenton J; Ehlert, Gregory J; Baur, Jeffery W

2015-12-23

A technique is reported for measuring and mapping the maximum internal temperature of a structural epoxy resin with high spatial resolution via the optically detected shape transformation of embedded gold nanorods (AuNRs). Spatially resolved absorption spectra of the nanocomposites are used to determine the frequencies of surface plasmon resonances. From these frequencies the AuNR aspect ratio is calculated using a new analytical approximation for the Mie-Gans scattering theory, which takes into account coincident changes in the local dielectric. Despite changes in the chemical environment, the calculated aspect ratio of the embedded nanorods is found to decrease over time to a steady-state value that depends linearly on the temperature over the range of 100-200 °C. Thus, the optical absorption can be used to determine the maximum temperature experienced at a particular location when exposure times exceed the temperature-dependent relaxation time. The usefulness of this approach is demonstrated by mapping the temperature of an internally heated structural epoxy resin with 10 μm lateral spatial resolution.

A spectral-knowledge-based approach for urban land-cover discrimination

NASA Technical Reports Server (NTRS)

Wharton, Stephen W.

1987-01-01

A prototype expert system was developed to demonstrate the feasibility of classifying multispectral remotely sensed data on the basis of spectral knowledge. The spectral expert was developed and tested with Thematic Mapper Simulator (TMS) data having eight spectral bands and a spatial resolution of 5 m. A knowledge base was developed that describes the target categories in terms of characteristic spectral relationships. The knowledge base was developed under the following assumptions: the data are calibrated to ground reflectance, the area is well illuminated, the pixels are dominated by a single category, and the target categories can be recognized without the use of spatial knowledge. Classification decisions are made on the basis of convergent evidence as derived from applying the spectral rules to a multiple spatial resolution representation of the image. The spectral expert achieved an accuracy of 80-percent correct or higher in recognizing 11 spectral categories in TMS data for the washington, DC, area. Classification performance can be expected to decrease for data that do not satisfy the above assumptions as illustrated by the 63-percent accuracy for 30-m resolution Thematic Mapper data.

A new vehicle emission inventory for China with high spatial and temporal resolution

NASA Astrophysics Data System (ADS)

Zheng, B.; Huo, H.; Zhang, Q.; Yao, Z. L.; Wang, X. T.; Yang, X. F.; Liu, H.; He, K. B.

2013-12-01

This study is the first in a series of papers that aim to develop high-resolution emission databases for different anthropogenic sources in China. Here we focus on on-road transportation. Because of the increasing impact of on-road transportation on regional air quality, developing an accurate and high-resolution vehicle emission inventory is important for both the research community and air quality management. This work proposes a new inventory methodology to improve the spatial and temporal accuracy and resolution of vehicle emissions in China. We calculate, for the first time, the monthly vehicle emissions (CO, NMHC, NOx, and PM2.5) for 2008 in 2364 counties (an administrative unit one level lower than city) by developing a set of approaches to estimate vehicle stock and monthly emission factors at county-level, and technology distribution at provincial level. We then introduce allocation weights for the vehicle kilometers traveled to assign the county-level emissions onto 0.05° × 0.05° grids based on the China Digital Road-network Map (CDRM). The new methodology overcomes the common shortcomings of previous inventory methods, including neglecting the geographical differences between key parameters and using surrogates that are weakly related to vehicle activities to allocate vehicle emissions. The new method has great advantages over previous methods in depicting the spatial distribution characteristics of vehicle activities and emissions. This work provides a better understanding of the spatial representation of vehicle emissions in China and can benefit both air quality modeling and management with improved spatial accuracy.

Application of geo-spatial technology in schistosomiasis modelling in Africa: a review.

PubMed

Manyangadze, Tawanda; Chimbari, Moses John; Gebreslasie, Michael; Mukaratirwa, Samson

2015-11-04

Schistosomiasis continues to impact socio-economic development negatively in sub-Saharan Africa. The advent of spatial technologies, including geographic information systems (GIS), Earth observation (EO) and global positioning systems (GPS) assist modelling efforts. However, there is increasing concern regarding the accuracy and precision of the current spatial models. This paper reviews the literature regarding the progress and challenges in the development and utilization of spatial technology with special reference to predictive models for schistosomiasis in Africa. Peer-reviewed papers identified through a PubMed search using the following keywords: geo-spatial analysis OR remote sensing OR modelling OR earth observation OR geographic information systems OR prediction OR mapping AND schistosomiasis AND Africa were used. Statistical uncertainty, low spatial and temporal resolution satellite data and poor validation were identified as some of the factors that compromise the precision and accuracy of the existing predictive models. The need for high spatial resolution of remote sensing data in conjunction with ancillary data viz. ground-measured climatic and environmental information, local presence/absence intermediate host snail surveys as well as prevalence and intensity of human infection for model calibration and validation are discussed. The importance of a multidisciplinary approach in developing robust, spatial data capturing, modelling techniques and products applicable in epidemiology is highlighted.

Restoring the spatial resolution of refocus images on 4D light field

NASA Astrophysics Data System (ADS)

Lim, JaeGuyn; Park, ByungKwan; Kang, JooYoung; Lee, SeongDeok

2010-01-01

This paper presents the method for generating a refocus image with restored spatial resolution on a plenoptic camera, which functions controlling the depth of field after capturing one image unlike a traditional camera. It is generally known that the camera captures 4D light field (angular and spatial information of light) within a limited 2D sensor and results in reducing 2D spatial resolution due to inevitable 2D angular data. That's the reason why a refocus image is composed of a low spatial resolution compared with 2D sensor. However, it has recently been known that angular data contain sub-pixel spatial information such that the spatial resolution of 4D light field can be increased. We exploit the fact for improving the spatial resolution of a refocus image. We have experimentally scrutinized that the spatial information is different according to the depth of objects from a camera. So, from the selection of refocused regions (corresponding depth), we use corresponding pre-estimated sub-pixel spatial information for reconstructing spatial resolution of the regions. Meanwhile other regions maintain out-of-focus. Our experimental results show the effect of this proposed method compared to existing method.

Precision of DVC approaches for strain analysis in bone imaged with μCT at different dimensional levels.

NASA Astrophysics Data System (ADS)

Dall'Ara, Enrico; Peña-Fernández, Marta; Palanca, Marco; Giorgi, Mario; Cristofolini, Luca; Tozzi, Gianluca

2017-11-01

Accurate measurement of local strain in heterogeneous and anisotropic bone tissue is fundamental to understand the pathophysiology of musculoskeletal diseases, to evaluate the effect of interventions from preclinical studies, and to optimize the design and delivery of biomaterials. Digital volume correlation (DVC) can be used to measure the three-dimensional displacement and strain fields from micro-Computed Tomography (µCT) images of loaded specimens. However, this approach is affected by the quality of the input images, by the morphology and density of the tissue under investigation, by the correlation scheme, and by the operational parameters used in the computation. Therefore, for each application the precision of the method should be evaluated. In this paper we present the results collected from datasets analyzed in previous studies as well as new data from a recent experimental campaign for characterizing the relationship between the precision of two different DVC approaches and the spatial resolution of the outputs. Different bone structures scanned with laboratory source µCT or Synchrotron light µCT (SRµCT) were processed in zero-strain tests to evaluate the precision of the DVC methods as a function of the subvolume size that ranged from 8 to 2500 micrometers. The results confirmed that for every microstructure the precision of DVC improves for larger subvolume size, following power laws. However, for the first time large differences in the precision of both local and global DVC approaches have been highlighted when SRµCT or in vivo µCT images were used instead of conventional ex vivo µCT. These findings suggest that in situ mechanical testing protocols applied in SRµCT facilities should be optimized in order to allow DVC analyses of localized strain measurements. Moreover, for in vivo µCT applications DVC analyses should be performed only with relatively course spatial resolution for achieving a reasonable precision of the method. In conclusion, we have extensively shown that the precision of both tested DVC approaches is affected by different bone structures, different input image resolution and different subvolume sizes. Before each specific application DVC users should always apply a similar approach to find the best compromise between precision and spatial resolution of the measurements.

High resolution in-operando microimaging of solar cells with pulsed electrically-detected magnetic resonance

NASA Astrophysics Data System (ADS)

Katz, Itai; Fehr, Matthias; Schnegg, Alexander; Lips, Klaus; Blank, Aharon

2015-02-01

The in-operando detection and high resolution spatial imaging of paramagnetic defects, impurities, and states becomes increasingly important for understanding loss mechanisms in solid-state electronic devices. Electron spin resonance (ESR), commonly employed for observing these species, cannot meet this challenge since it suffers from limited sensitivity and spatial resolution. An alternative and much more sensitive method, called electrically-detected magnetic resonance (EDMR), detects the species through their magnetic fingerprint, which can be traced in the device's electrical current. However, until now it could not obtain high resolution images in operating electronic devices. In this work, the first spatially-resolved electrically-detected magnetic resonance images (EDMRI) of paramagnetic states in an operating real-world electronic device are provided. The presented method is based on a novel microwave pulse sequence allowing for the coherent electrical detection of spin echoes in combination with powerful pulsed magnetic-field gradients. The applicability of the method is demonstrated on a device-grade 1-μm-thick amorphous silicon (a-Si:H) solar cell and an identical device that was degraded locally by an electron beam. The degraded areas with increased concentrations of paramagnetic defects lead to a local increase in recombination that is mapped by EDMRI with ∼20-μm-scale pixel resolution. The novel approach presented here can be widely used in the nondestructive in-operando three-dimensional characterization of solid-state electronic devices with a resolution potential of less than 100 nm.

Adaptive multiresolution modeling of groundwater flow in heterogeneous porous media

NASA Astrophysics Data System (ADS)

Malenica, Luka; Gotovac, Hrvoje; Srzic, Veljko; Andric, Ivo

2016-04-01

Proposed methodology was originally developed by our scientific team in Split who designed multiresolution approach for analyzing flow and transport processes in highly heterogeneous porous media. The main properties of the adaptive Fup multi-resolution approach are: 1) computational capabilities of Fup basis functions with compact support capable to resolve all spatial and temporal scales, 2) multi-resolution presentation of heterogeneity as well as all other input and output variables, 3) accurate, adaptive and efficient strategy and 4) semi-analytical properties which increase our understanding of usually complex flow and transport processes in porous media. The main computational idea behind this approach is to separately find the minimum number of basis functions and resolution levels necessary to describe each flow and transport variable with the desired accuracy on a particular adaptive grid. Therefore, each variable is separately analyzed, and the adaptive and multi-scale nature of the methodology enables not only computational efficiency and accuracy, but it also describes subsurface processes closely related to their understood physical interpretation. The methodology inherently supports a mesh-free procedure, avoiding the classical numerical integration, and yields continuous velocity and flux fields, which is vitally important for flow and transport simulations. In this paper, we will show recent improvements within the proposed methodology. Since "state of the art" multiresolution approach usually uses method of lines and only spatial adaptive procedure, temporal approximation was rarely considered as a multiscale. Therefore, novel adaptive implicit Fup integration scheme is developed, resolving all time scales within each global time step. It means that algorithm uses smaller time steps only in lines where solution changes are intensive. Application of Fup basis functions enables continuous time approximation, simple interpolation calculations across different temporal lines and local time stepping control. Critical aspect of time integration accuracy is construction of spatial stencil due to accurate calculation of spatial derivatives. Since common approach applied for wavelets and splines uses a finite difference operator, we developed here collocation one including solution values and differential operator. In this way, new improved algorithm is adaptive in space and time enabling accurate solution for groundwater flow problems, especially in highly heterogeneous porous media with large lnK variances and different correlation length scales. In addition, differences between collocation and finite volume approaches are discussed. Finally, results show application of methodology to the groundwater flow problems in highly heterogeneous confined and unconfined aquifers.

High Spatial Resolution Commercial Satellite Imaging Product Characterization

NASA Technical Reports Server (NTRS)

Ryan, Robert E.; Pagnutti, Mary; Blonski, Slawomir; Ross, Kenton W.; Stnaley, Thomas

2005-01-01

NASA Stennis Space Center's Remote Sensing group has been characterizing privately owned high spatial resolution multispectral imaging systems, such as IKONOS, QuickBird, and OrbView-3. Natural and man made targets were used for spatial resolution, radiometric, and geopositional characterizations. Higher spatial resolution also presents significant adjacency effects for accurate reliable radiometry.

Simulation of Population-Based Commuter Exposure to NO2 Using Different Air Pollution Models

PubMed Central

Ragettli, Martina S.; Tsai, Ming-Yi; Braun-Fahrländer, Charlotte; de Nazelle, Audrey; Schindler, Christian; Ineichen, Alex; Ducret-Stich, Regina E.; Perez, Laura; Probst-Hensch, Nicole; Künzli, Nino; Phuleria, Harish C.

2014-01-01

We simulated commuter routes and long-term exposure to traffic-related air pollution during commute in a representative population sample in Basel (Switzerland), and evaluated three air pollution models with different spatial resolution for estimating commute exposures to nitrogen dioxide (NO2) as a marker of long-term exposure to traffic-related air pollution. Our approach includes spatially and temporally resolved data on actual commuter routes, travel modes and three air pollution models. Annual mean NO2 commuter exposures were similar between models. However, we found more within-city and within-subject variability in annual mean (±SD) NO2 commuter exposure with a high resolution dispersion model (40 ± 7 µg m−3, range: 21–61) than with a dispersion model with a lower resolution (39 ± 5 µg m−3; range: 24–51), and a land use regression model (41 ± 5 µg m−3; range: 24–54). Highest median cumulative exposures were calculated along motorized transport and bicycle routes, and the lowest for walking. For estimating commuter exposure within a city and being interested also in small-scale variability between roads, a model with a high resolution is recommended. For larger scale epidemiological health assessment studies, models with a coarser spatial resolution are likely sufficient, especially when study areas include suburban and rural areas. PMID:24823664

Mars-solar wind interaction: LatHyS, an improved parallel 3-D multispecies hybrid model

NASA Astrophysics Data System (ADS)

Modolo, Ronan; Hess, Sebastien; Mancini, Marco; Leblanc, Francois; Chaufray, Jean-Yves; Brain, David; Leclercq, Ludivine; Esteban-Hernández, Rosa; Chanteur, Gerard; Weill, Philippe; González-Galindo, Francisco; Forget, Francois; Yagi, Manabu; Mazelle, Christian

2016-07-01

In order to better represent Mars-solar wind interaction, we present an unprecedented model achieving spatial resolution down to 50 km, a so far unexplored resolution for global kinetic models of the Martian ionized environment. Such resolution approaches the ionospheric plasma scale height. In practice, the model is derived from a first version described in Modolo et al. (2005). An important effort of parallelization has been conducted and is presented here. A better description of the ionosphere was also implemented including ionospheric chemistry, electrical conductivities, and a drag force modeling the ion-neutral collisions in the ionosphere. This new version of the code, named LatHyS (Latmos Hybrid Simulation), is here used to characterize the impact of various spatial resolutions on simulation results. In addition, and following a global model challenge effort, we present the results of simulation run for three cases which allow addressing the effect of the suprathermal corona and of the solar EUV activity on the magnetospheric plasma boundaries and on the global escape. Simulation results showed that global patterns are relatively similar for the different spatial resolution runs, but finest grid runs provide a better representation of the ionosphere and display more details of the planetary plasma dynamic. Simulation results suggest that a significant fraction of escaping O+ ions is originated from below 1200 km altitude.

Enhancing hyperspectral spatial resolution using multispectral image fusion: A wavelet approach

NASA Astrophysics Data System (ADS)

Jazaeri, Amin

High spectral and spatial resolution images have a significant impact in remote sensing applications. Because both spatial and spectral resolutions of spaceborne sensors are fixed by design and it is not possible to further increase the spatial or spectral resolution, techniques such as image fusion must be applied to achieve such goals. This dissertation introduces the concept of wavelet fusion between hyperspectral and multispectral sensors in order to enhance the spectral and spatial resolution of a hyperspectral image. To test the robustness of this concept, images from Hyperion (hyperspectral sensor) and Advanced Land Imager (multispectral sensor) were first co-registered and then fused using different wavelet algorithms. A regression-based fusion algorithm was also implemented for comparison purposes. The results show that the fused images using a combined bi-linear wavelet-regression algorithm have less error than other methods when compared to the ground truth. In addition, a combined regression-wavelet algorithm shows more immunity to misalignment of the pixels due to the lack of proper registration. The quantitative measures of average mean square error show that the performance of wavelet-based methods degrades when the spatial resolution of hyperspectral images becomes eight times less than its corresponding multispectral image. Regardless of what method of fusion is utilized, the main challenge in image fusion is image registration, which is also a very time intensive process. Because the combined regression wavelet technique is computationally expensive, a hybrid technique based on regression and wavelet methods was also implemented to decrease computational overhead. However, the gain in faster computation was offset by the introduction of more error in the outcome. The secondary objective of this dissertation is to examine the feasibility and sensor requirements for image fusion for future NASA missions in order to be able to perform onboard image fusion. In this process, the main challenge of image registration was resolved by registering the input images using transformation matrices of previously acquired data. The composite image resulted from the fusion process remarkably matched the ground truth, indicating the possibility of real time onboard fusion processing.

Scalable and fast heterogeneous molecular simulation with predictive parallelization schemes

NASA Astrophysics Data System (ADS)

Guzman, Horacio V.; Junghans, Christoph; Kremer, Kurt; Stuehn, Torsten

2017-11-01

Multiscale and inhomogeneous molecular systems are challenging topics in the field of molecular simulation. In particular, modeling biological systems in the context of multiscale simulations and exploring material properties are driving a permanent development of new simulation methods and optimization algorithms. In computational terms, those methods require parallelization schemes that make a productive use of computational resources for each simulation and from its genesis. Here, we introduce the heterogeneous domain decomposition approach, which is a combination of an heterogeneity-sensitive spatial domain decomposition with an a priori rearrangement of subdomain walls. Within this approach, the theoretical modeling and scaling laws for the force computation time are proposed and studied as a function of the number of particles and the spatial resolution ratio. We also show the new approach capabilities, by comparing it to both static domain decomposition algorithms and dynamic load-balancing schemes. Specifically, two representative molecular systems have been simulated and compared to the heterogeneous domain decomposition proposed in this work. These two systems comprise an adaptive resolution simulation of a biomolecule solvated in water and a phase-separated binary Lennard-Jones fluid.

Comparison of Two Grid Refinement Approaches for High Resolution Regional Climate Modeling: MPAS vs WRF

NASA Astrophysics Data System (ADS)

Leung, L.; Hagos, S. M.; Rauscher, S.; Ringler, T.

2012-12-01

This study compares two grid refinement approaches using global variable resolution model and nesting for high-resolution regional climate modeling. The global variable resolution model, Model for Prediction Across Scales (MPAS), and the limited area model, Weather Research and Forecasting (WRF) model, are compared in an idealized aqua-planet context with a focus on the spatial and temporal characteristics of tropical precipitation simulated by the models using the same physics package from the Community Atmosphere Model (CAM4). For MPAS, simulations have been performed with a quasi-uniform resolution global domain at coarse (1 degree) and high (0.25 degree) resolution, and a variable resolution domain with a high-resolution region at 0.25 degree configured inside a coarse resolution global domain at 1 degree resolution. Similarly, WRF has been configured to run on a coarse (1 degree) and high (0.25 degree) resolution tropical channel domain as well as a nested domain with a high-resolution region at 0.25 degree nested two-way inside the coarse resolution (1 degree) tropical channel. The variable resolution or nested simulations are compared against the high-resolution simulations that serve as virtual reality. Both MPAS and WRF simulate 20-day Kelvin waves propagating through the high-resolution domains fairly unaffected by the change in resolution. In addition, both models respond to increased resolution with enhanced precipitation. Grid refinement induces zonal asymmetry in precipitation (heating), accompanied by zonal anomalous Walker like circulations and standing Rossby wave signals. However, there are important differences between the anomalous patterns in MPAS and WRF due to differences in the grid refinement approaches and sensitivity of model physics to grid resolution. This study highlights the need for "scale aware" parameterizations in variable resolution and nested regional models.

Latin Hypercube Sampling (LHS) at variable resolutions for enhanced watershed scale Soil Sampling and Digital Soil Mapping.

NASA Astrophysics Data System (ADS)

Hamalainen, Sampsa; Geng, Xiaoyuan; He, Juanxia

2017-04-01

Latin Hypercube Sampling (LHS) at variable resolutions for enhanced watershed scale Soil Sampling and Digital Soil Mapping. Sampsa Hamalainen, Xiaoyuan Geng, and Juanxia, He. AAFC - Agriculture and Agr-Food Canada, Ottawa, Canada. The Latin Hypercube Sampling (LHS) approach to assist with Digital Soil Mapping has been developed for some time now, however the purpose of this work was to complement LHS with use of multiple spatial resolutions of covariate datasets and variability in the range of sampling points produced. This allowed for specific sets of LHS points to be produced to fulfil the needs of various partners from multiple projects working in the Ontario and Prince Edward Island provinces of Canada. Secondary soil and environmental attributes are critical inputs that are required in the development of sampling points by LHS. These include a required Digital Elevation Model (DEM) and subsequent covariate datasets produced as a result of a Digital Terrain Analysis performed on the DEM. These additional covariates often include but are not limited to Topographic Wetness Index (TWI), Length-Slope (LS) Factor, and Slope which are continuous data. The range of specific points created in LHS included 50 - 200 depending on the size of the watershed and more importantly the number of soil types found within. The spatial resolution of covariates included within the work ranged from 5 - 30 m. The iterations within the LHS sampling were run at an optimal level so the LHS model provided a good spatial representation of the environmental attributes within the watershed. Also, additional covariates were included in the Latin Hypercube Sampling approach which is categorical in nature such as external Surficial Geology data. Some initial results of the work include using a 1000 iteration variable within the LHS model. 1000 iterations was consistently a reasonable value used to produce sampling points that provided a good spatial representation of the environmental attributes. When working within the same spatial resolution for covariates, however only modifying the desired number of sampling points produced, the change of point location portrayed a strong geospatial relationship when using continuous data. Access to agricultural fields and adjacent land uses is often "pinned" as the greatest deterrent to performing soil sampling for both soil survey and soil attribute validation work. The lack of access can be a result of poor road access and/or difficult geographical conditions to navigate for field work individuals. This seems a simple yet continuous issue to overcome for the scientific community and in particular, soils professionals. The ability to assist with the ease of access to sampling points will be in the future a contribution to the Latin Hypercube Sampling (LHS) approach. By removing all locations in the initial instance from the DEM, the LHS model can be restricted to locations only with access from the adjacent road or trail. To further the approach, a road network geospatial dataset can be included within spatial Geographic Information Systems (GIS) applications to access already produced points using a shortest-distance network method.

Automated Verification of Spatial Resolution in Remotely Sensed Imagery

NASA Technical Reports Server (NTRS)

Davis, Bruce; Ryan, Robert; Holekamp, Kara; Vaughn, Ronald

2011-01-01

Image spatial resolution characteristics can vary widely among sources. In the case of aerial-based imaging systems, the image spatial resolution characteristics can even vary between acquisitions. In these systems, aircraft altitude, speed, and sensor look angle all affect image spatial resolution. Image spatial resolution needs to be verified with estimators that include the ground sample distance (GSD), the modulation transfer function (MTF), and the relative edge response (RER), all of which are key components of image quality, along with signal-to-noise ratio (SNR) and dynamic range. Knowledge of spatial resolution parameters is important to determine if features of interest are distinguishable in imagery or associated products, and to develop image restoration algorithms. An automated Spatial Resolution Verification Tool (SRVT) was developed to rapidly determine the spatial resolution characteristics of remotely sensed aerial and satellite imagery. Most current methods for assessing spatial resolution characteristics of imagery rely on pre-deployed engineered targets and are performed only at selected times within preselected scenes. The SRVT addresses these insufficiencies by finding uniform, high-contrast edges from urban scenes and then using these edges to determine standard estimators of spatial resolution, such as the MTF and the RER. The SRVT was developed using the MATLAB programming language and environment. This automated software algorithm assesses every image in an acquired data set, using edges found within each image, and in many cases eliminating the need for dedicated edge targets. The SRVT automatically identifies high-contrast, uniform edges and calculates the MTF and RER of each image, and when possible, within sections of an image, so that the variation of spatial resolution characteristics across the image can be analyzed. The automated algorithm is capable of quickly verifying the spatial resolution quality of all images within a data set, enabling the appropriate use of those images in a number of applications.

Computationally Efficient Adaptive Beamformer for Ultrasound Imaging Based on QR Decomposition.

PubMed

Park, Jongin; Wi, Seok-Min; Lee, Jin S

2016-02-01

Adaptive beamforming methods for ultrasound imaging have been studied to improve image resolution and contrast. The most common approach is the minimum variance (MV) beamformer which minimizes the power of the beamformed output while maintaining the response from the direction of interest constant. The method achieves higher resolution and better contrast than the delay-and-sum (DAS) beamformer, but it suffers from high computational cost. This cost is mainly due to the computation of the spatial covariance matrix and its inverse, which requires O(L(3)) computations, where L denotes the subarray size. In this study, we propose a computationally efficient MV beamformer based on QR decomposition. The idea behind our approach is to transform the spatial covariance matrix to be a scalar matrix σI and we subsequently obtain the apodization weights and the beamformed output without computing the matrix inverse. To do that, QR decomposition algorithm is used and also can be executed at low cost, and therefore, the computational complexity is reduced to O(L(2)). In addition, our approach is mathematically equivalent to the conventional MV beamformer, thereby showing the equivalent performances. The simulation and experimental results support the validity of our approach.

Resolution Enhancement of Hyperion Hyperspectral Data using Ikonos Multispectral Data

DTIC Science & Technology

2007-09-01

spatial - resolution hyperspectral image to produce a sharpened product. The result is a product that has the spectral properties of the ...multispectral sensors. In this work, we examine the benefits of combining data from high- spatial - resolution , low- spectral - resolution spectral imaging...sensors with data obtained from high- spectral - resolution , low- spatial - resolution spectral imaging sensors.

Thematic and spatial resolutions affect model-based predictions of tree species distribution.

PubMed

Liang, Yu; He, Hong S; Fraser, Jacob S; Wu, ZhiWei

2013-01-01

Subjective decisions of thematic and spatial resolutions in characterizing environmental heterogeneity may affect the characterizations of spatial pattern and the simulation of occurrence and rate of ecological processes, and in turn, model-based tree species distribution. Thus, this study quantified the importance of thematic and spatial resolutions, and their interaction in predictions of tree species distribution (quantified by species abundance). We investigated how model-predicted species abundances changed and whether tree species with different ecological traits (e.g., seed dispersal distance, competitive capacity) had different responses to varying thematic and spatial resolutions. We used the LANDIS forest landscape model to predict tree species distribution at the landscape scale and designed a series of scenarios with different thematic (different numbers of land types) and spatial resolutions combinations, and then statistically examined the differences of species abundance among these scenarios. Results showed that both thematic and spatial resolutions affected model-based predictions of species distribution, but thematic resolution had a greater effect. Species ecological traits affected the predictions. For species with moderate dispersal distance and relatively abundant seed sources, predicted abundance increased as thematic resolution increased. However, for species with long seeding distance or high shade tolerance, thematic resolution had an inverse effect on predicted abundance. When seed sources and dispersal distance were not limiting, the predicted species abundance increased with spatial resolution and vice versa. Results from this study may provide insights into the choice of thematic and spatial resolutions for model-based predictions of tree species distribution.

Thematic and Spatial Resolutions Affect Model-Based Predictions of Tree Species Distribution

PubMed Central

Liang, Yu; He, Hong S.; Fraser, Jacob S.; Wu, ZhiWei

2013-01-01

Subjective decisions of thematic and spatial resolutions in characterizing environmental heterogeneity may affect the characterizations of spatial pattern and the simulation of occurrence and rate of ecological processes, and in turn, model-based tree species distribution. Thus, this study quantified the importance of thematic and spatial resolutions, and their interaction in predictions of tree species distribution (quantified by species abundance). We investigated how model-predicted species abundances changed and whether tree species with different ecological traits (e.g., seed dispersal distance, competitive capacity) had different responses to varying thematic and spatial resolutions. We used the LANDIS forest landscape model to predict tree species distribution at the landscape scale and designed a series of scenarios with different thematic (different numbers of land types) and spatial resolutions combinations, and then statistically examined the differences of species abundance among these scenarios. Results showed that both thematic and spatial resolutions affected model-based predictions of species distribution, but thematic resolution had a greater effect. Species ecological traits affected the predictions. For species with moderate dispersal distance and relatively abundant seed sources, predicted abundance increased as thematic resolution increased. However, for species with long seeding distance or high shade tolerance, thematic resolution had an inverse effect on predicted abundance. When seed sources and dispersal distance were not limiting, the predicted species abundance increased with spatial resolution and vice versa. Results from this study may provide insights into the choice of thematic and spatial resolutions for model-based predictions of tree species distribution. PMID:23861828

Towards Mapping the Ocean Surface Topography at 1 km Resolution

NASA Technical Reports Server (NTRS)

Fu, Lee-Lueng; Rodriquez, Ernesto

2006-01-01

We propose to apply the technique of synthetic aperture radar interferometry to the measurement of ocean surface topography at spatial resolution approaching 1 km. The measurement will have wide ranging applications in oceanography, hydrology, and marine geophysics. The oceanographic and related societal applications are briefly discussed in the paper. To meet the requirements for oceanographic applications, the instrument must be flown in an orbit with proper sampling of ocean tides.

Direct nano-patterning of graphene with helium ion beams

NASA Astrophysics Data System (ADS)

Naitou, Y.; Iijima, T.; Ogawa, S.

2015-01-01

Helium ion microscopy (HIM) was used for direct nano-patterning of single-layer graphene (SLG) on SiO2/Si substrates. This technique involves irradiation of the sample with accelerated helium ions (He+). Doses of 2.0 × 1016 He+ cm-2 from a 30 kV beam induced a metal-insulator transition in the SLG. The resolution of HIM patterning on SLG was investigated by fabricating nanoribbons and nanostructures. Analysis of scanning capacitance microscopy measurements revealed that the spatial resolution of HIM patterning depended on the dosage of He+ in a non-monotonic fashion. Increasing the dose from 2.0 × 1016 to 5.0 × 1016 He+ cm-2 improved the spatial resolution to several tens of nanometers. However, doses greater than 1.0 × 1017 He+ cm-2 degraded the patterning characteristics. Direct patterning using HIM is a versatile approach to graphene fabrication and can be applied to graphene-based devices.

Sub-10 fs Time-Resolved Vibronic Optical Microscopy

PubMed Central

2016-01-01

We introduce femtosecond wide-field transient absorption microscopy combining sub-10 fs pump and probe pulses covering the complete visible (500–650 nm) and near-infrared (650–950 nm) spectrum with diffraction-limited optical resolution. We demonstrate the capabilities of our system by reporting the spatially- and spectrally-resolved transient electronic response of MAPbI3–xClx perovskite films and reveal significant quenching of the transient bleach signal at grain boundaries. The unprecedented temporal resolution enables us to directly observe the formation of band-gap renormalization, completed in 25 fs after photoexcitation. In addition, we acquire hyperspectral Raman maps of TIPS pentacene films with sub-400 nm spatial and sub-15 cm–1 spectral resolution covering the 100–2000 cm–1 window. Our approach opens up the possibility of studying ultrafast dynamics on nanometer length and femtosecond time scales in a variety of two-dimensional and nanoscopic systems. PMID:27934055

Scattered electrons in microscopy and microanalysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ottensmeyer, F.P.

The use of scattered electrons alone for direct imaging of biological specimens makes it possible to obtain structural information at atomic and near-atomic spatial resolutions of 0.3 to 0.5 nanometer. While this is not as good as the resolution possible with x-ray crystallography, such an approach provides structural information rapidly on individual macromolecules that have not been, and possibly cannot be, crystallized. Analysis of the spectrum of energies of scattered electrons and imaging of the latter with characteristic energy bands within the spectrum produces a powerful new technique of atomic microanalysis. This technique, which has a spatial resolution of aboutmore » 0.5 nanometer and a minimum detection sensitivity of about 50 atoms of phosphorus, is especially useful for light atom analysis and appears to have applications in molecular biology, cell biology, histology, pathology, botany, and many other fields.« less

Scattered electrons in microscopy and microanalysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ottensmeyer, F.P.

The use of scattered electrons alone for direct imaging of biological specimens makes it possible to obtain structural information at atomic and near-atomic spatial resolutions of 0.3 to 0.5 nanometer. While this is not as good as the resolution possible with x-ray crystallography, such an approach provides structural information rapidly on individual macromolecules that have not been, and possibly cannot be, crystallized. Analysis of the spectrum of energies of scattered electrons and imaging of the latter with characteristic energy bands within the spectrum produce a powerful new technique of atomic microanalysis. This technique, which has a spatial resolution of aboutmore » 0.5 nanometer and a minimum detection sensitivity of about 50 atoms of phosphorus, is especially useful for light atom analysis and appears to have applications in molecular biology, cell biology, histology, pathology, botany, and many other fields.« less

Spatial Variability of Soil-Water Storage in the Southern Sierra Critical Zone Observatory: Measurement and Prediction

NASA Astrophysics Data System (ADS)

Oroza, C.; Bales, R. C.; Zheng, Z.; Glaser, S. D.

2017-12-01

Predicting the spatial distribution of soil moisture in mountain environments is confounded by multiple factors, including complex topography, spatial variably of soil texture, sub-surface flow paths, and snow-soil interactions. While remote-sensing tools such as passive-microwave monitoring can measure spatial variability of soil moisture, they only capture near-surface soil layers. Large-scale sensor networks are increasingly providing soil-moisture measurements at high temporal resolution across a broader range of depths than are accessible from remote sensing. It may be possible to combine these in-situ measurements with high-resolution LIDAR topography and canopy cover to estimate the spatial distribution of soil moisture at high spatial resolution at multiple depths. We study the feasibility of this approach using six years (2009-2014) of daily volumetric water content measurements at 10-, 30-, and 60-cm depths from the Southern Sierra Critical Zone Observatory. A non-parametric, multivariate regression algorithm, Random Forest, was used to predict the spatial distribution of depth-integrated soil-water storage, based on the in-situ measurements and a combination of node attributes (topographic wetness, northness, elevation, soil texture, and location with respect to canopy cover). We observe predictable patterns of predictor accuracy and independent variable ranking during the six-year study period. Predictor accuracy is highest during the snow-cover and early recession periods but declines during the dry period. Soil texture has consistently high feature importance. Other landscape attributes exhibit seasonal trends: northness peaks during the wet-up period, and elevation and topographic-wetness index peak during the recession and dry period, respectively.

Radiofrequency Ablation, MR Thermometry, and High-Spatial-Resolution MR Parametric Imaging with a Single, Minimally Invasive Device.

PubMed

Ertürk, M Arcan; Sathyanarayana Hegde, Shashank; Bottomley, Paul A

2016-12-01

Purpose To develop and demonstrate in vitro and in vivo a single interventional magnetic resonance (MR)-active device that integrates the functions of precise identification of a tissue site with the delivery of radiofrequency (RF) energy for ablation, high-spatial-resolution thermal mapping to monitor thermal dose, and quantitative MR imaging relaxometry to document ablation-induced tissue changes for characterizing ablated tissue. Materials and Methods All animal studies were approved by the institutional animal care and use committee. A loopless MR imaging antenna composed of a tuned microcable either 0.8 or 2.2 mm in diameter with an extended central conductor was switched between a 3-T MR imaging unit and an RF power source to monitor and perform RF ablation in bovine muscle and human artery samples in vitro and in rabbits in vivo. High-spatial-resolution (250-300-μm) proton resonance frequency shift MR thermometry was interleaved with ablations. Quantitative spin-lattice (T1) and spin-spin (T2) relaxation time MR imaging mapping was performed before and after ablation. These maps were compared with findings from gross tissue examination of the region of ablated tissue after MR imaging. Results High-spatial-resolution MR imaging afforded temperature mapping in less than 8 seconds for monitoring ablation temperatures in excess of 85°C delivered by the same device. This produced irreversible thermal injury and necrosis. Quantitative MR imaging relaxation time maps demonstrated up to a twofold variation in mean regional T1 and T2 after ablation versus before ablation. Conclusion A simple, integrated, minimally invasive interventional probe that provides image-guided therapy delivery, thermal mapping of dose, and detection of ablation-associated MR imaging parametric changes was developed and demonstrated. With this single-device approach, coupling-related safety concerns associated with multiple conductor approaches were avoided. © RSNA, 2016 Online supplemental material is available for this article.

Puzzle Imaging: Using Large-Scale Dimensionality Reduction Algorithms for Localization

PubMed Central

Glaser, Joshua I.; Zamft, Bradley M.; Church, George M.; Kording, Konrad P.

2015-01-01

Current high-resolution imaging techniques require an intact sample that preserves spatial relationships. We here present a novel approach, “puzzle imaging,” that allows imaging a spatially scrambled sample. This technique takes many spatially disordered samples, and then pieces them back together using local properties embedded within the sample. We show that puzzle imaging can efficiently produce high-resolution images using dimensionality reduction algorithms. We demonstrate the theoretical capabilities of puzzle imaging in three biological scenarios, showing that (1) relatively precise 3-dimensional brain imaging is possible; (2) the physical structure of a neural network can often be recovered based only on the neural connectivity matrix; and (3) a chemical map could be reproduced using bacteria with chemosensitive DNA and conjugative transfer. The ability to reconstruct scrambled images promises to enable imaging based on DNA sequencing of homogenized tissue samples. PMID:26192446

Comparison of spatial association approaches for landscape mapping of soil organic carbon stocks

NASA Astrophysics Data System (ADS)

Miller, B. A.; Koszinski, S.; Wehrhan, M.; Sommer, M.

2015-03-01

The distribution of soil organic carbon (SOC) can be variable at small analysis scales, but consideration of its role in regional and global issues demands the mapping of large extents. There are many different strategies for mapping SOC, among which is to model the variables needed to calculate the SOC stock indirectly or to model the SOC stock directly. The purpose of this research is to compare direct and indirect approaches to mapping SOC stocks from rule-based, multiple linear regression models applied at the landscape scale via spatial association. The final products for both strategies are high-resolution maps of SOC stocks (kg m-2), covering an area of 122 km2, with accompanying maps of estimated error. For the direct modelling approach, the estimated error map was based on the internal error estimations from the model rules. For the indirect approach, the estimated error map was produced by spatially combining the error estimates of component models via standard error propagation equations. We compared these two strategies for mapping SOC stocks on the basis of the qualities of the resulting maps as well as the magnitude and distribution of the estimated error. The direct approach produced a map with less spatial variation than the map produced by the indirect approach. The increased spatial variation represented by the indirect approach improved R2 values for the topsoil and subsoil stocks. Although the indirect approach had a lower mean estimated error for the topsoil stock, the mean estimated error for the total SOC stock (topsoil + subsoil) was lower for the direct approach. For these reasons, we recommend the direct approach to modelling SOC stocks be considered a more conservative estimate of the SOC stocks' spatial distribution.

Comparison of spatial association approaches for landscape mapping of soil organic carbon stocks

NASA Astrophysics Data System (ADS)

Miller, B. A.; Koszinski, S.; Wehrhan, M.; Sommer, M.

2014-11-01

The distribution of soil organic carbon (SOC) can be variable at small analysis scales, but consideration of its role in regional and global issues demands the mapping of large extents. There are many different strategies for mapping SOC, among which are to model the variables needed to calculate the SOC stock indirectly or to model the SOC stock directly. The purpose of this research is to compare direct and indirect approaches to mapping SOC stocks from rule-based, multiple linear regression models applied at the landscape scale via spatial association. The final products for both strategies are high-resolution maps of SOC stocks (kg m-2), covering an area of 122 km2, with accompanying maps of estimated error. For the direct modelling approach, the estimated error map was based on the internal error estimations from the model rules. For the indirect approach, the estimated error map was produced by spatially combining the error estimates of component models via standard error propagation equations. We compared these two strategies for mapping SOC stocks on the basis of the qualities of the resulting maps as well as the magnitude and distribution of the estimated error. The direct approach produced a map with less spatial variation than the map produced by the indirect approach. The increased spatial variation represented by the indirect approach improved R2 values for the topsoil and subsoil stocks. Although the indirect approach had a lower mean estimated error for the topsoil stock, the mean estimated error for the total SOC stock (topsoil + subsoil) was lower for the direct approach. For these reasons, we recommend the direct approach to modelling SOC stocks be considered a more conservative estimate of the SOC stocks' spatial distribution.

Construction of pixel-level resolution DEMs from monocular images by shape and albedo from shading constrained with low-resolution DEM

NASA Astrophysics Data System (ADS)

Wu, Bo; Liu, Wai Chung; Grumpe, Arne; Wöhler, Christian

2018-06-01

Lunar Digital Elevation Model (DEM) is important for lunar successful landing and exploration missions. Lunar DEMs are typically generated by photogrammetry or laser altimetry approaches. Photogrammetric methods require multiple stereo images of the region of interest and it may not be applicable in cases where stereo coverage is not available. In contrast, reflectance based shape reconstruction techniques, such as shape from shading (SfS) and shape and albedo from shading (SAfS), apply monocular images to generate DEMs with pixel-level resolution. We present a novel hierarchical SAfS method that refines a lower-resolution DEM to pixel-level resolution given a monocular image with known light source. We also estimate the corresponding pixel-wise albedo map in the process and based on that to regularize the shape reconstruction with pixel-level resolution based on the low-resolution DEM. In this study, a Lunar-Lambertian reflectance model is applied to estimate the albedo map. Experiments were carried out using monocular images from the Lunar Reconnaissance Orbiter Narrow Angle Camera (LRO NAC), with spatial resolution of 0.5-1.5 m per pixel, constrained by the Selenological and Engineering Explorer and LRO Elevation Model (SLDEM), with spatial resolution of 60 m. The results indicate that local details are well recovered by the proposed algorithm with plausible albedo estimation. The low-frequency topographic consistency depends on the quality of low-resolution DEM and the resolution difference between the image and the low-resolution DEM.

AVAL - The ASTER Volcanic Ash Library

NASA Astrophysics Data System (ADS)

Williams, D.; Ramsey, M. S.

2016-12-01

Volcanic ash is a rich data source for understanding the causal mechanisms behind volcanic eruptions. Petrologic and morphometric information can provide direct information on the characteristics of the parent magma. Understanding how erupted ash interacts with the atmosphere can help quantify the effect that explosive volcanism has on the local to regional climate, whereas a measure of the particle size distribution enables more accurate modeling of plume propagation. Remote sensing is regularly employed to monitor volcanic plumes using a suite of high temporal/low spatial resolution sensors. These methods employ radiative transfer modeling with assumptions of the transmissive properties of infrared energy through the plume to determine ash density, particle size and sulfur dioxide content. However, such approaches are limited to the optically-transparent regions, and the low spatial resolution data are only useful for large-scale trends. In a new approach, we are treating the infrared-opaque regions of the plume in a similar way to a solid emitting surface. This allows high spatial resolution orbital thermal infrared data from the dense proximal plume to be modeled using a linear deconvolution approach coupled with a spectral library to extract the particle size and petrology. The newly created ASTER Volcanic Ash Library (AVAL) provides the end member spectral suite, and is comprised of laboratory emission measurements of volcanic ash taken from a variety of different volcanic settings, to obtain a wide range of petrologies. These samples have been further subdivided into particle size fractions to account for spectral changes due to diffraction effects. Once mapped to the ASTER sensor's spectral resolution, this library is applied to image data and the plume deconvolved to estimate composition and particle size. We have analyzed eruptions at the Soufrière Hills Volcano, Montserrat, Chaitén and Puyehue-Cordón Caulle, both Chile, and Eyjafjallajökull, Iceland. These results provide particle size distributions within actively-erupting volcanic plumes for the first time in high resolution, and the petrologic information is being studied to understand the underlying eruptive processes observed.

Description and properties of a resistive network applied to emission tomography detector readouts

NASA Astrophysics Data System (ADS)

Boisson, F.; Bekaert, V.; Sahr, J.; Brasse, D.

2017-11-01

Over the last twenty years, PET systems have used discrete crystal detector modules coupled to multi-channel photodetectors, mostly to improve the spatial resolution. Although reading each readout channels individually would be of great interest, costs associated with the electronics would, in most cases, be too expensive. It is therefore essential to propose lower-cost solutions that do not degrade the overall system's performance. One possible solution to reduce the development costs of a PET system without degrading performance is the use of a resistive network which reduces the total number of readout channels. In this study, we present a symmetric charge division resistive network and associated software methods to assess the performance of a PET detector. Our approach consists in keeping the n lines and n columns information provided by a symmetric charge division circuit (SCD). We provided equations relative to output currents of the network, which enable estimation of the charge. We propose a novel approach to reconstruct the charge distribution from the lines and columns projection using a maximum likelihood expectation maximization (MLEM) approach which takes the non-uniformity of the photodetector channel gains into account. We also introduce a mathematical proof of the relation between the sigma of the reconstructed charge distribution and the Ratio between the line of interest (maximum value) and the background signal charges. To the best of our knowledge, this is the first study reporting these equations. Preliminary results obtained with a resistive network used in readout of a monolithic 50 × 50 × 8mm3 LYSO crystal coupled to a H9500 PMT validated the effectiveness of the reconstructed charge distribution to optimize both the x and y spatial resolution and the energy resolution. We obtained a mean x and y spatial resolution of 1.10 mm FWHM and a 14.7% energy resolution by calculating the integral of the reconstructed charge distribution. Finally, the relation between the ratio and the sigma of the reconstructed charge distribution may provide new opportunities in terms of Depth-of-Interaction estimation when using a monolithic crystal coupled to a multi-channel photodetector.

On the creation of high spatial resolution imaging spectroscopy data from multi-temporal low spatial resolution imagery

NASA Astrophysics Data System (ADS)

Yao, Wei; van Aardt, Jan; Messinger, David

2017-05-01

The Hyperspectral Infrared Imager (HyspIRI) mission aims to provide global imaging spectroscopy data to the benefit of especially ecosystem studies. The onboard spectrometer will collect radiance spectra from the visible to short wave infrared (VSWIR) regions (400-2500 nm). The mission calls for fine spectral resolution (10 nm band width) and as such will enable scientists to perform material characterization, species classification, and even sub-pixel mapping. However, the global coverage requirement results in a relatively low spatial resolution (GSD 30m), which restricts applications to objects of similar scales. We therefore have focused on the assessment of sub-pixel vegetation structure from spectroscopy data in past studies. In this study, we investigate the development or reconstruction of higher spatial resolution imaging spectroscopy data via fusion of multi-temporal data sets to address the drawbacks implicit in low spatial resolution imagery. The projected temporal resolution of the HyspIRI VSWIR instrument is 15 days, which implies that we have access to as many as six data sets for an area over the course of a growth season. Previous studies have shown that select vegetation structural parameters, e.g., leaf area index (LAI) and gross ecosystem production (GEP), are relatively constant in summer and winter for temperate forests; we therefore consider the data sets collected in summer to be from a similar, stable forest structure. The first step, prior to fusion, involves registration of the multi-temporal data. A data fusion algorithm then can be applied to the pre-processed data sets. The approach hinges on an algorithm that has been widely applied to fuse RGB images. Ideally, if we have four images of a scene which all meet the following requirements - i) they are captured with the same camera configurations; ii) the pixel size of each image is x; and iii) at least r2 images are aligned on a grid of x/r - then a high-resolution image, with a pixel size of x/r, can be reconstructed from the multi-temporal set. The algorithm was applied to data from NASA's classic Airborne Visible and Infrared Imaging Spectrometer (AVIRIS-C; GSD 18m), collected between 2013-2015 (summer and fall) over our study area (NEON's Southwest Pacific Domain; Fresno, CA) to generate higher spatial resolution imagery (GSD 9m). The reconstructed data set was validated via comparison to NEON's imaging spectrometer (NIS) data (GSD 1m). The results showed that algorithm worked well with the AVIRIS-C data and could be applied to the HyspIRI data.

A new multiscale approach for monitoring vegetation using remote sensing-based indicators in laboratory, field, and landscape.

PubMed

Lausch, Angela; Pause, Marion; Merbach, Ines; Zacharias, Steffen; Doktor, Daniel; Volk, Martin; Seppelt, Ralf

2013-02-01

Remote sensing is an important tool for studying patterns in surface processes on different spatiotemporal scales. However, differences in the spatiospectral and temporal resolution of remote sensing data as well as sensor-specific surveying characteristics very often hinder comparative analyses and effective up- and downscaling analyses. This paper presents a new methodical framework for combining hyperspectral remote sensing data on different spatial and temporal scales. We demonstrate the potential of using the "One Sensor at Different Scales" (OSADIS) approach for the laboratory (plot), field (local), and landscape (regional) scales. By implementing the OSADIS approach, we are able (1) to develop suitable stress-controlled vegetation indices for selected variables such as the Leaf Area Index (LAI), chlorophyll, photosynthesis, water content, nutrient content, etc. over a whole vegetation period. Focused laboratory monitoring can help to document additive and counteractive factors and processes of the vegetation and to correctly interpret their spectral response; (2) to transfer the models obtained to the landscape level; (3) to record imaging hyperspectral information on different spatial scales, achieving a true comparison of the structure and process results; (4) to minimize existing errors from geometrical, spectral, and temporal effects due to sensor- and time-specific differences; and (5) to carry out a realistic top- and downscaling by determining scale-dependent correction factors and transfer functions. The first results of OSADIS experiments are provided by controlled whole vegetation experiments on barley under water stress on the plot scale to model LAI using the vegetation indices Normalized Difference Vegetation Index (NDVI) and green NDVI (GNDVI). The regression model ascertained from imaging hyperspectral AISA-EAGLE/HAWK (DUAL) data was used to model LAI. This was done by using the vegetation index GNDVI with an R (2) of 0.83, which was transferred to airborne hyperspectral data on the local and regional scales. For this purpose, hyperspectral imagery was collected at three altitudes over a land cover gradient of 25 km within a timeframe of a few minutes, yielding a spatial resolution from 1 to 3 m. For all recorded spatial scales, both the LAI and the NDVI were determined. The spatial properties of LAI and NDVI of all recorded hyperspectral images were compared using semivariance metrics derived from the variogram. The first results show spatial differences in the heterogeneity of LAI and NDVI from 1 to 3 m with the recorded hyperspectral data. That means that differently recorded data on different scales might not sufficiently maintain the spatial properties of high spatial resolution hyperspectral images.

Groundwater nitrate pollution: High-resolution approach of calculating the nitrogen balance surplus for Germany

NASA Astrophysics Data System (ADS)

Klement, Laura; Bach, Martin; Breuer, Lutz; Häußermann, Uwe

2017-04-01

The latest inventory of the EU Water Framework Directive determined that 26.3% of Germany's groundwater bodies are in a poor chemical state regarding nitrate. As of late October 2016, the European Commission has filed a lawsuit against Germany for not taking appropriate measures against high nitrate levels in water bodies and thus failing to comply with the EU Nitrate Directive. Due to over-fertilization and high-density animal production, Agriculture was identified as the main source of nitrate pollution. One way to characterize the potential impact of reactive nitrogen on water bodies is the soil surface nitrogen balance where all agricultural nitrogen inputs within an area are contrasted with the output, i.e. the harvest. The surplus nitrogen (given in kg N per ha arable land and year) can potentially leach into the groundwater and thus can be used as a risk indicator. In order to develop and advocate appropriate measures to mitigate the agricultural nitrogen surplus with spatial precision, high-resolution data for the nitrogen surplus is needed. In Germany, not all nitrogen input data is available with the required spatial resolution, especially the use of mineral fertilizers is only given statewide. Therefore, some elements of the nitrogen balance need to be estimated based on agricultural statistics. Hitherto, statistics from the Federal Statistical Office and the statistical offices of the 16 federal states of Germany were used to calculate the soil surface balance annually for the spatial resolution of the 402 districts of Germany (mean size 890 km2). In contrast, this study presents an approach to estimate the nitrogen surplus at a much higher spatial resolution by using the comprehensive Agricultural census data collected in 2010 providing data for 326000 agricultural holdings. This resulted in a nitrogen surplus map with a 5 km x 5 km grid which was subsequently used to calculate the nitrogen concentration of percolation water. This provides a considerably more detailed insight on regions where the groundwater is particularly vulnerable to nitrate pollution and appropriate measures are most needed.

A cloud mask methodology for high resolution remote sensing data combining information from high and medium resolution optical sensors

NASA Astrophysics Data System (ADS)

Sedano, Fernando; Kempeneers, Pieter; Strobl, Peter; Kucera, Jan; Vogt, Peter; Seebach, Lucia; San-Miguel-Ayanz, Jesús

2011-09-01

This study presents a novel cloud masking approach for high resolution remote sensing images in the context of land cover mapping. As an advantage to traditional methods, the approach does not rely on thermal bands and it is applicable to images from most high resolution earth observation remote sensing sensors. The methodology couples pixel-based seed identification and object-based region growing. The seed identification stage relies on pixel value comparison between high resolution images and cloud free composites at lower spatial resolution from almost simultaneously acquired dates. The methodology was tested taking SPOT4-HRVIR, SPOT5-HRG and IRS-LISS III as high resolution images and cloud free MODIS composites as reference images. The selected scenes included a wide range of cloud types and surface features. The resulting cloud masks were evaluated through visual comparison. They were also compared with ad-hoc independently generated cloud masks and with the automatic cloud cover assessment algorithm (ACCA). In general the results showed an agreement in detected clouds higher than 95% for clouds larger than 50 ha. The approach produced consistent results identifying and mapping clouds of different type and size over various land surfaces including natural vegetation, agriculture land, built-up areas, water bodies and snow.

Efficient Probabilistic Forecasting for High-Resolution Models through Clustered-State Data Assimilation

NASA Astrophysics Data System (ADS)

Hamidi, A.; Grossberg, M.; Khanbilvardi, R.

2016-12-01

Flood response in an urban area is the product of interactions of spatially and temporally varying rainfall and infrastructures. In urban areas, however, the complex sub-surface networks of tunnels, waste and storm water drainage systems are often inaccessible, pose challenges for modeling and prediction of the drainage infrastructure performance. The increased availability of open data in cities is an emerging information asset for a better understanding of the dynamics of urban water drainage infrastructure. This includes crowd sourced data and community reporting. A well-known source of this type of data is the non-emergency hotline "311" which is available in many US cities, and may contain information pertaining to the performance of physical facilities, condition of the environment, or residents' experience, comfort and well-being. In this study, seven years of New York City 311 (NYC311) call during 2010-2016 is employed, as an alternative approach for identifying the areas of the city most prone to sewer back up flooding. These zones are compared with the hydrologic analysis of runoff flooding zones to provide a predictive model for the City. The proposed methodology is an example of urban system phenomenology using crowd sourced, open data. A novel algorithm for calculating the spatial distribution of flooding complaints across NYC's five boroughs is presented in this study. In this approach, the features that represent reporting bias are separated from those that relate to actual infrastructure system performance. The sewer backup results are assessed with the spatial distribution of runoff in NYC during 2010-2016. With advances in radar technologies, a high spatial-temporal resolution data set for precipitation is available for most of the United States that can be implemented in hydrologic analysis of dense urban environments. High resolution gridded Stage IV radar rainfall data along with the high resolution spatially distributed land cover data are employed to investigate the urban pluvial flooding. The monthly results of excess runoff are compared with the sewer backup in NYC to build a predictive model of flood zones according to the 311 phone calls.

Retrieved Products from Simulated Hyperspectral Observations of a Hurricane

NASA Technical Reports Server (NTRS)

Susskind, Joel; Kouvaris, Louis; Iredell, Lena; Blaisdell, John

2015-01-01

Demonstrate via Observing System Simulation Experiments (OSSEs) the potential utility of flying high spatial resolution AIRS class IR sounders on future LEO and GEO missions.The study simulates and analyzes radiances for 3 sounders with AIRS spectral and radiometric properties on different orbits with different spatial resolutions: 1) Control run 13 kilometers AIRS spatial resolution at nadir on LEO in Aqua orbit; 2) 2 kilometer spatial resolution LEO sounder at nadir ARIES; 3) 5 kilometers spatial resolution sounder on a GEO orbit, radiances simulated every 72 minutes.

Innovating Big Data Computing Geoprocessing for Analysis of Engineered-Natural Systems

NASA Astrophysics Data System (ADS)

Rose, K.; Baker, V.; Bauer, J. R.; Vasylkivska, V.

2016-12-01

Big data computing and analytical techniques offer opportunities to improve predictions about subsurface systems while quantifying and characterizing associated uncertainties from these analyses. Spatial analysis, big data and otherwise, of subsurface natural and engineered systems are based on variable resolution, discontinuous, and often point-driven data to represent continuous phenomena. We will present examples from two spatio-temporal methods that have been adapted for use with big datasets and big data geo-processing capabilities. The first approach uses regional earthquake data to evaluate spatio-temporal trends associated with natural and induced seismicity. The second algorithm, the Variable Grid Method (VGM), is a flexible approach that presents spatial trends and patterns, such as those resulting from interpolation methods, while simultaneously visualizing and quantifying uncertainty in the underlying spatial datasets. In this presentation we will show how we are utilizing Hadoop to store and perform spatial analyses to efficiently consume and utilize large geospatial data in these custom analytical algorithms through the development of custom Spark and MapReduce applications that incorporate ESRI Hadoop libraries. The team will present custom `Big Data' geospatial applications that run on the Hadoop cluster and integrate with ESRI ArcMap with the team's probabilistic VGM approach. The VGM-Hadoop tool has been specially built as a multi-step MapReduce application running on the Hadoop cluster for the purpose of data reduction. This reduction is accomplished by generating multi-resolution, non-overlapping, attributed topology that is then further processed using ESRI's geostatistical analyst to convey a probabilistic model of a chosen study region. Finally, we will share our approach for implementation of data reduction and topology generation via custom multi-step Hadoop applications, performance benchmarking comparisons, and Hadoop-centric opportunities for greater parallelization of geospatial operations.

Fine‐resolution conservation planning with limited climate‐change information

USGS Publications Warehouse

Shah, Payal; Mallory, Mindy L.; Ando , Amy W.; Guntenspergen, Glenn R.

2017-01-01

Climate‐change induced uncertainties in future spatial patterns of conservation‐related outcomes make it difficult to implement standard conservation‐planning paradigms. A recent study translates Markowitz's risk‐diversification strategy from finance to conservation settings, enabling conservation agents to use this diversification strategy for allocating conservation and restoration investments across space to minimize the risk associated with such uncertainty. However, this method is information intensive and requires a large number of forecasts of ecological outcomes associated with possible climate‐change scenarios for carrying out fine‐resolution conservation planning. We developed a technique for iterative, spatial portfolio analysis that can be used to allocate scarce conservation resources across a desired level of subregions in a planning landscape in the absence of a sufficient number of ecological forecasts. We applied our technique to the Prairie Pothole Region in central North America. A lack of sufficient future climate information prevented attainment of the most efficient risk‐return conservation outcomes in the Prairie Pothole Region. The difference in expected conservation returns between conservation planning with limited climate‐change information and full climate‐change information was as large as 30% for the Prairie Pothole Region even when the most efficient iterative approach was used. However, our iterative approach allowed finer resolution portfolio allocation with limited climate‐change forecasts such that the best possible risk‐return combinations were obtained. With our most efficient iterative approach, the expected loss in conservation outcomes owing to limited climate‐change information could be reduced by 17% relative to other iterative approaches.

Spatial Variation in Mobility-Lifetime Product in Bulk TlBr and CZT

NASA Astrophysics Data System (ADS)

Phillips, David; Haegel, Nancy; Blaine, Kevin; Kim, Hadong; Ciampi, Guido; Cirignano, Len

2012-02-01

The energy resolution of a semiconductor radiation detector depends on the charge transport properties of the semiconductor, and the mobility-lifetime (μτ) product is a key figure of merit for charge transport. In this work, we investigate the effects of two impurities, Na and Cu, on the μτ product in bulk thallium bromide (TlBr) using cathodoluminescence (CL) and transport imaging. Transport imaging uses a scanning electron microscope to generate a line of charge carriers on the surface of a bulk sample, and the intensity and spatial distribution of the recombination luminescence are recorded. A Green's function approach is used to model the generation, diffusion, and recombination of charge carriers under steady-state conditions. The luminescence distribution is fit to the model to extract the ambipolar diffusion length and the μτ product, providing a high-resolution correlation between the luminescence variations due to dopants/defects and the quantitative transport behavior. The μτ product has been mapped across a 40 μm segment of TlBr at a resolution of 2 μm. Additionally, this approach has been used to locally map variations in ambipolar diffusion length and μτ product due to extended defects in cadmium zinc telluride (CZT).

High Resolution Eddy-Current Wire Testing Based on a Gmr Sensor-Array

NASA Astrophysics Data System (ADS)

Kreutzbruck, Marc; Allweins, Kai; Strackbein, Chris; Bernau, Hendrick

2009-03-01

Increasing demands in materials quality and cost effectiveness have led to advanced standards in manufacturing technology. Especially when dealing with high quality standards in conjunction with high throughput quantitative NDE techniques are vital to provide reliable and fast quality control systems. In this work we illuminate a modern electromagnetic NDE approach using a small GMR sensor array for testing superconducting wires. Four GMR sensors are positioned around the wire. Each GMR sensor provides a field sensitivity of 200 pT/√Hz and a spatial resolution of about 100 μm. This enables us to detect under surface defects of 100 μm in size in a depth of 200 μm with a signal-to-noise ratio of better than 400. Surface defects could be detected with a SNR of up to 10,000. Besides this remarkably SNR the small extent of GMR sensors results in a spatial resolution which offers new visualisation techniques for defect localisation, defect characterization and tomography-like mapping techniques. We also report on inverse algorithms based on either a Finite Element Method or an analytical approach. These allow for accurate defect localization on the urn scale and an estimation of the defect size.

High Resolution Insights into Snow Distribution Provided by Drone Photogrammetry

NASA Astrophysics Data System (ADS)

Redpath, T.; Sirguey, P. J.; Cullen, N. J.; Fitzsimons, S.

2017-12-01

Dynamic in time and space, New Zealand's seasonal snow is largely confined to remote alpine areas, complicating ongoing in situ measurement and characterisation. Improved understanding and modeling of the seasonal snowpack requires fine scale resolution of snow distribution and spatial variability. The potential of remotely piloted aircraft system (RPAS) photogrammetry to resolve spatial and temporal variability of snow depth and water equivalent in a New Zealand alpine catchment is assessed in the Pisa Range, Central Otago. This approach yielded orthophotomosaics and digital surface models (DSM) at 0.05 and 0.15 m spatial resolution, respectively. An autumn reference DSM allowed mapping of winter (02/08/2016) and spring (10/09/2016) snow depth at 0.15 m spatial resolution, via DSM differencing. The consistency and accuracy of the RPAS-derived surface was assessed by comparison of snow-free regions of the spring and autumn DSMs, while accuracy of RPAS retrieved snow depth was assessed with 86 in situ snow probe measurements. Results show a mean vertical residual of 0.024 m between DSMs acquired in autumn and spring. This residual approximated a Laplace distribution, reflecting the influence of large outliers on the small overall bias. Propagation of errors associated with successive DSMs saw snow depth mapped with an accuracy of ± 0.09 m (95% c.l.). Comparing RPAS and in situ snow depth measurements revealed the influence of geo-location uncertainty and interactions between vegetation and the snowpack on snow depth uncertainty and bias. Semi-variogram analysis revealed that the RPAS outperformed systematic in situ measurements in resolving fine scale spatial variability. Despite limitations accompanying RPAS photogrammetry, this study demonstrates a repeatable means of accurately mapping snow depth for an entire, yet relatively small, hydrological basin ( 0.5 km2), at high resolution. Resolving snowpack features associated with re-distribution and preferential accumulation and ablation, snow depth maps provide geostatistically robust insights into seasonal snow processes, with unprecedented detail. Such data may enhance understanding of physical processes controlling spatial and temporal distribution of seasonal snow, and their relative importance at varying spatial and temporal scales.

Evaluating a Local Ensemble Transform Kalman Filter snow cover data assimilation method to estimate SWE within a high-resolution hydrologic modeling framework across Western US mountainous regions

NASA Astrophysics Data System (ADS)

Oaida, C. M.; Andreadis, K.; Reager, J. T., II; Famiglietti, J. S.; Levoe, S.

2017-12-01

Accurately estimating how much snow water equivalent (SWE) is stored in mountainous regions characterized by complex terrain and snowmelt-driven hydrologic cycles is not only greatly desirable, but also a big challenge. Mountain snowpack exhibits high spatial variability across a broad range of spatial and temporal scales due to a multitude of physical and climatic factors, making it difficult to observe or estimate in its entirety. Combing remotely sensed data and high resolution hydrologic modeling through data assimilation (DA) has the potential to provide a spatially and temporally continuous SWE dataset at horizontal scales that capture sub-grid snow spatial variability and are also relevant to stakeholders such as water resource managers. Here, we present the evaluation of a new snow DA approach that uses a Local Ensemble Transform Kalman Filter (LETKF) in tandem with the Variable Infiltration Capacity macro-scale hydrologic model across the Western United States, at a daily temporal resolution, and a horizontal resolution of 1.75 km x 1.75 km. The LETKF is chosen for its relative simplicity, ease of implementation, and computational efficiency and scalability. The modeling/DA system assimilates daily MODIS Snow Covered Area and Grain Size (MODSCAG) fractional snow cover over, and has been developed to efficiently calculate SWE estimates over extended periods of time and covering large regional-scale areas at relatively high spatial resolution, ultimately producing a snow reanalysis-type dataset. Here we focus on the assessment of SWE produced by the DA scheme over several basins in California's Sierra Nevada Mountain range where Airborne Snow Observatory data is available, during the last five water years (2013-2017), which include both one of the driest and one of the wettest years. Comparison against such a spatially distributed SWE observational product provides a greater understanding of the model's ability to estimate SWE and SWE spatial variability, and highlights under which conditions snow cover DA can add value in estimating SWE.

Assumption-versus data-based approaches to summarizing species' ranges.

PubMed

Peterson, A Townsend; Navarro-Sigüenza, Adolfo G; Gordillo, Alejandro

2018-06-01

For conservation decision making, species' geographic distributions are mapped using various approaches. Some such efforts have downscaled versions of coarse-resolution extent-of-occurrence maps to fine resolutions for conservation planning. We examined the quality of the extent-of-occurrence maps as range summaries and the utility of refining those maps into fine-resolution distributional hypotheses. Extent-of-occurrence maps tend to be overly simple, omit many known and well-documented populations, and likely frequently include many areas not holding populations. Refinement steps involve typological assumptions about habitat preferences and elevational ranges of species, which can introduce substantial error in estimates of species' true areas of distribution. However, no model-evaluation steps are taken to assess the predictive ability of these models, so model inaccuracies are not noticed. Whereas range summaries derived by these methods may be useful in coarse-grained, global-extent studies, their continued use in on-the-ground conservation applications at fine spatial resolutions is not advisable in light of reliance on assumptions, lack of real spatial resolution, and lack of testing. In contrast, data-driven techniques that integrate primary data on biodiversity occurrence with remotely sensed data that summarize environmental dimensions (i.e., ecological niche modeling or species distribution modeling) offer data-driven solutions based on a minimum of assumptions that can be evaluated and validated quantitatively to offer a well-founded, widely accepted method for summarizing species' distributional patterns for conservation applications. © 2016 Society for Conservation Biology.

The importance of taxonomic resolution for additive beta diversity as revealed through DNA barcoding.

PubMed

Bringloe, Trevor T; Cottenie, Karl; Martin, Gillian K; Adamowicz, Sarah J

2016-12-01

Additive diversity partitioning (α, β, and γ) is commonly used to study the distribution of species-level diversity across spatial scales. Here, we first investigate whether published studies of additive diversity partitioning show signs of difficulty attaining species-level resolution due to inherent limitations with morphological identifications. Second, we present a DNA barcoding approach to delineate specimens of stream caddisfly larvae (order Trichoptera) and consider the importance of taxonomic resolution on classical (additive) measures of beta (β) diversity. Caddisfly larvae were sampled using a hierarchical spatial design in two regions (subarctic Churchill, Manitoba, Canada; temperate Pennsylvania, USA) and then additively partitioned according to Barcode Index Numbers (molecular clusters that serve as a proxy for species), genus, and family levels; diversity components were expressed as proportional species turnover. We screened 114 articles of additive diversity partitioning and found that a third reported difficulties with achieving species-level identifications, with a clear taxonomic tendency towards challenges identifying invertebrate taxa. Regarding our own study, caddisfly BINs appeared to show greater subregional turnover (e.g., proportional additive β) compared to genus or family levels. Diversity component studies failing to achieve species resolution due to morphological identifications may therefore be underestimating diversity turnover at larger spatial scales.

Comparative Geostatistical Analysis of Flowmeter and Direct-Push Hydraulic Conductivity Profiles at the MADE Site

NASA Astrophysics Data System (ADS)

Bohling, G.; Liu, G.; Knobbe, S. J.; Reboulet, E. C.; Hyndman, D. W.; Dietrich, P.; Butler, J. J.

2010-12-01

Spatial variations in hydraulic conductivity (K) are a critical control on subsurface solute transport. Characterization of such variations at the resolution (cm to dm) required for transport investigations, however, has proven to be a formidable challenge. A new generation of direct-push (DP) tools has now been developed for the characterization of vertical K variations at this resolution. These tools, which can be run in high- (0.015-m) and low- (0.4 m) resolution modes, were recently applied to the extensively studied and highly heterogeneous MADE site. Results from a geostatistical analysis of 64 DP K profiles compare favorably with the flowmeter K data that have served as the primary basis for previous MADE studies. The global statistics of the low-resolution DP and flowmeter K data are in excellent agreement. The correlation structures for the high-resolution DP data show excellent agreement with those computed from the flowmeter data. However, the geometric mean DP K value for high-resolution profiling is roughly one order of magnitude lower than the geometric mean flowmeter K value, possibly as a result of the biases inherent in each approach compounded with differences in the areal distribution of flowmeter and DP profile locations. A DP profile through the MADE aquifer to a depth of 12 m can be completed as rapidly as 1.5-2 hours, a small fraction of the time required to obtain a single flowmeter profile when well drilling, installation, and development are considered. The results of this study demonstrate that DP profiling is a practically feasible approach for characterization of spatial variations in K at the resolution required for transport investigations in highly heterogeneous systems.

Multishot Targeted PROPELLER Magnetic Resonance Imaging: Description of the Technique and Initial Applications

PubMed Central

Deng, Jie; Larson, Andrew C.

2010-01-01

Objectives To test the feasibility of combining inner-volume imaging (IVI) techniques with conventional multishot periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) techniques for targeted-PROPELLER magnetic resonance imaging. Materials and Methods Perpendicular section-selective gradients for spatially selective excitation and refocusing RF pulses were applied to limit the refocused field-of-view (FOV) along the phase-encoding direction for each rectangular blade image. We performed comparison studies in phantoms and normal volunteers by using targeted-PROPELLER methods for a wide range of imaging applications that commonly use turbo-spin-echo (TSE) approaches (brain, abdominal, vessel wall, cardiac). Results In these initial studies, we demonstrated the feasibility of using targeted-PROPELLER approaches to limit the imaging FOV thereby reducing the number of blades or permitting increased spatial resolution without commensurate increases in scan time. Both phantom and in vivo motion studies demonstrated the potential for more robust regional self-navigated motion correction compared with conventional full FOV PROPELLER methods. Conclusion We demonstrated that the reduced FOV targeted-PROPELLER technique offers the potential for reducing imaging time, increasing spatial resolution, and targeting specific areas for robust regional motion correction. PMID:19465860

An advanced stochastic weather generator for simulating 2-D high-resolution climate variables

NASA Astrophysics Data System (ADS)

Peleg, Nadav; Fatichi, Simone; Paschalis, Athanasios; Molnar, Peter; Burlando, Paolo

2017-07-01

A new stochastic weather generator, Advanced WEather GENerator for a two-dimensional grid (AWE-GEN-2d) is presented. The model combines physical and stochastic approaches to simulate key meteorological variables at high spatial and temporal resolution: 2 km × 2 km and 5 min for precipitation and cloud cover and 100 m × 100 m and 1 h for near-surface air temperature, solar radiation, vapor pressure, atmospheric pressure, and near-surface wind. The model requires spatially distributed data for the calibration process, which can nowadays be obtained by remote sensing devices (weather radar and satellites), reanalysis data sets and ground stations. AWE-GEN-2d is parsimonious in terms of computational demand and therefore is particularly suitable for studies where exploring internal climatic variability at multiple spatial and temporal scales is fundamental. Applications of the model include models of environmental systems, such as hydrological and geomorphological models, where high-resolution spatial and temporal meteorological forcing is crucial. The weather generator was calibrated and validated for the Engelberg region, an area with complex topography in the Swiss Alps. Model test shows that the climate variables are generated by AWE-GEN-2d with a level of accuracy that is sufficient for many practical applications.

High resolution population distribution maps for Southeast Asia in 2010 and 2015.

PubMed

Gaughan, Andrea E; Stevens, Forrest R; Linard, Catherine; Jia, Peng; Tatem, Andrew J

2013-01-01

Spatially accurate, contemporary data on human population distributions are vitally important to many applied and theoretical researchers. The Southeast Asia region has undergone rapid urbanization and population growth over the past decade, yet existing spatial population distribution datasets covering the region are based principally on population count data from censuses circa 2000, with often insufficient spatial resolution or input data to map settlements precisely. Here we outline approaches to construct a database of GIS-linked circa 2010 census data and methods used to construct fine-scale (∼100 meters spatial resolution) population distribution datasets for each country in the Southeast Asia region. Landsat-derived settlement maps and land cover information were combined with ancillary datasets on infrastructure to model population distributions for 2010 and 2015. These products were compared with those from two other methods used to construct commonly used global population datasets. Results indicate mapping accuracies are consistently higher when incorporating land cover and settlement information into the AsiaPop modelling process. Using existing data, it is possible to produce detailed, contemporary and easily updatable population distribution datasets for Southeast Asia. The 2010 and 2015 datasets produced are freely available as a product of the AsiaPop Project and can be downloaded from: www.asiapop.org.

High Resolution Population Distribution Maps for Southeast Asia in 2010 and 2015

PubMed Central

Gaughan, Andrea E.; Stevens, Forrest R.; Linard, Catherine; Jia, Peng; Tatem, Andrew J.

2013-01-01

Spatially accurate, contemporary data on human population distributions are vitally important to many applied and theoretical researchers. The Southeast Asia region has undergone rapid urbanization and population growth over the past decade, yet existing spatial population distribution datasets covering the region are based principally on population count data from censuses circa 2000, with often insufficient spatial resolution or input data to map settlements precisely. Here we outline approaches to construct a database of GIS-linked circa 2010 census data and methods used to construct fine-scale (∼100 meters spatial resolution) population distribution datasets for each country in the Southeast Asia region. Landsat-derived settlement maps and land cover information were combined with ancillary datasets on infrastructure to model population distributions for 2010 and 2015. These products were compared with those from two other methods used to construct commonly used global population datasets. Results indicate mapping accuracies are consistently higher when incorporating land cover and settlement information into the AsiaPop modelling process. Using existing data, it is possible to produce detailed, contemporary and easily updatable population distribution datasets for Southeast Asia. The 2010 and 2015 datasets produced are freely available as a product of the AsiaPop Project and can be downloaded from: www.asiapop.org. PMID:23418469

A STED-FLIM microscope applied to imaging the natural killer cell immune synapse

NASA Astrophysics Data System (ADS)

Lenz, M. O.; Brown, A. C. N.; Auksorius, E.; Davis, D. M.; Dunsby, C.; Neil, M. A. A.; French, P. M. W.

2011-03-01

We present a stimulated emission depletion (STED) fluorescence lifetime imaging (FLIM) microscope, excited by a microstructured optical fibre supercontinuum source that is pumped by a femtosecond Ti:Sapphire-laser, which is also used for depletion. Implemented using a piezo-scanning stage on a laser scanning confocal fluorescence microscope system with FLIM realised using time correlated single photon counting (TCSPC), this provides convenient switching between confocal and STED-FLIM with spatial resolution down to below 60 nm. We will present our design considerations to make a robust instrument for biological applications including a comparison between fixed phase plate and spatial light modulator (SLM) approaches to shape the STED beam and the correlation of STED and confocal FLIM microscopy. Following our previous application of FLIM-FRET to study intercellular signalling at the immunological synapse (IS), we are employing STED microscopy to characterize the spatial distribution of cellular molecules with subdiffraction resolution at the IS. In particular, we are imaging cytoskeletal structure at the Natural Killer cell activated immune synapse. We will also present our progress towards multilabel STED microscopy to determine how relative spatial molecular organization, previously undetectable by conventional microscopy techniques, is important for NK cell cytotoxic function. Keywords: STED, Stimulated Emission Depletion Microscopy, Natural Killer (NK) cell, Fluorescence lifetime imaging, FLIM, Super resolution microscopy.

An effective approach for gap-filling continental scale remotely sensed time-series

PubMed Central

Weiss, Daniel J.; Atkinson, Peter M.; Bhatt, Samir; Mappin, Bonnie; Hay, Simon I.; Gething, Peter W.

2014-01-01

The archives of imagery and modeled data products derived from remote sensing programs with high temporal resolution provide powerful resources for characterizing inter- and intra-annual environmental dynamics. The impressive depth of available time-series from such missions (e.g., MODIS and AVHRR) affords new opportunities for improving data usability by leveraging spatial and temporal information inherent to longitudinal geospatial datasets. In this research we develop an approach for filling gaps in imagery time-series that result primarily from cloud cover, which is particularly problematic in forested equatorial regions. Our approach consists of two, complementary gap-filling algorithms and a variety of run-time options that allow users to balance competing demands of model accuracy and processing time. We applied the gap-filling methodology to MODIS Enhanced Vegetation Index (EVI) and daytime and nighttime Land Surface Temperature (LST) datasets for the African continent for 2000–2012, with a 1 km spatial resolution, and an 8-day temporal resolution. We validated the method by introducing and filling artificial gaps, and then comparing the original data with model predictions. Our approach achieved R2 values above 0.87 even for pixels within 500 km wide introduced gaps. Furthermore, the structure of our approach allows estimation of the error associated with each gap-filled pixel based on the distance to the non-gap pixels used to model its fill value, thus providing a mechanism for including uncertainty associated with the gap-filling process in downstream applications of the resulting datasets. PMID:25642100

Isotropic-resolution linear-array-based photoacoustic computed tomography through inverse Radon transform

NASA Astrophysics Data System (ADS)

Li, Guo; Xia, Jun; Li, Lei; Wang, Lidai; Wang, Lihong V.

2015-03-01

Linear transducer arrays are readily available for ultrasonic detection in photoacoustic computed tomography. They offer low cost, hand-held convenience, and conventional ultrasonic imaging. However, the elevational resolution of linear transducer arrays, which is usually determined by the weak focus of the cylindrical acoustic lens, is about one order of magnitude worse than the in-plane axial and lateral spatial resolutions. Therefore, conventional linear scanning along the elevational direction cannot provide high-quality three-dimensional photoacoustic images due to the anisotropic spatial resolutions. Here we propose an innovative method to achieve isotropic resolutions for three-dimensional photoacoustic images through combined linear and rotational scanning. In each scan step, we first elevationally scan the linear transducer array, and then rotate the linear transducer array along its center in small steps, and scan again until 180 degrees have been covered. To reconstruct isotropic three-dimensional images from the multiple-directional scanning dataset, we use the standard inverse Radon transform originating from X-ray CT. We acquired a three-dimensional microsphere phantom image through the inverse Radon transform method and compared it with a single-elevational-scan three-dimensional image. The comparison shows that our method improves the elevational resolution by up to one order of magnitude, approaching the in-plane lateral-direction resolution. In vivo rat images were also acquired.

Thermal Physical Property-Based Fusion of Geostationary Meteorological Satellite Visible and Infrared Channel Images

PubMed Central

Han, Lei; Shi, Lu; Yang, Yiling; Song, Dalei

2014-01-01

Geostationary meteorological satellite infrared (IR) channel data contain important spectral information for meteorological research and applications, but their spatial resolution is relatively low. The objective of this study is to obtain higher-resolution IR images. One common method of increasing resolution fuses the IR data with high-resolution visible (VIS) channel data. However, most existing image fusion methods focus only on visual performance, and often fail to take into account the thermal physical properties of the IR images. As a result, spectral distortion occurs frequently. To tackle this problem, we propose a thermal physical properties-based correction method for fusing geostationary meteorological satellite IR and VIS images. In our two-step process, the high-resolution structural features of the VIS image are first extracted and incorporated into the IR image using regular multi-resolution fusion approach, such as the multiwavelet analysis. This step significantly increases the visual details in the IR image, but fake thermal information may be included. Next, the Stefan-Boltzmann Law is applied to correct the distortion, to retain or recover the thermal infrared nature of the fused image. The results of both the qualitative and quantitative evaluation demonstrate that the proposed physical correction method both improves the spatial resolution and preserves the infrared thermal properties. PMID:24919017

Thermal physical property-based fusion of geostationary meteorological satellite visible and infrared channel images.

PubMed

Han, Lei; Shi, Lu; Yang, Yiling; Song, Dalei

2014-06-10

Geostationary meteorological satellite infrared (IR) channel data contain important spectral information for meteorological research and applications, but their spatial resolution is relatively low. The objective of this study is to obtain higher-resolution IR images. One common method of increasing resolution fuses the IR data with high-resolution visible (VIS) channel data. However, most existing image fusion methods focus only on visual performance, and often fail to take into account the thermal physical properties of the IR images. As a result, spectral distortion occurs frequently. To tackle this problem, we propose a thermal physical properties-based correction method for fusing geostationary meteorological satellite IR and VIS images. In our two-step process, the high-resolution structural features of the VIS image are first extracted and incorporated into the IR image using regular multi-resolution fusion approach, such as the multiwavelet analysis. This step significantly increases the visual details in the IR image, but fake thermal information may be included. Next, the Stefan-Boltzmann Law is applied to correct the distortion, to retain or recover the thermal infrared nature of the fused image. The results of both the qualitative and quantitative evaluation demonstrate that the proposed physical correction method both improves the spatial resolution and preserves the infrared thermal properties.

A Parameter Estimation Scheme for Multiscale Kalman Smoother (MKS) Algorithm Used in Precipitation Data Fusion

NASA Technical Reports Server (NTRS)

Wang, Shugong; Liang, Xu

2013-01-01

A new approach is presented in this paper to effectively obtain parameter estimations for the Multiscale Kalman Smoother (MKS) algorithm. This new approach has demonstrated promising potentials in deriving better data products based on data of different spatial scales and precisions. Our new approach employs a multi-objective (MO) parameter estimation scheme (called MO scheme hereafter), rather than using the conventional maximum likelihood scheme (called ML scheme) to estimate the MKS parameters. Unlike the ML scheme, the MO scheme is not simply built on strict statistical assumptions related to prediction errors and observation errors, rather, it directly associates the fused data of multiple scales with multiple objective functions in searching best parameter estimations for MKS through optimization. In the MO scheme, objective functions are defined to facilitate consistency among the fused data at multiscales and the input data at their original scales in terms of spatial patterns and magnitudes. The new approach is evaluated through a Monte Carlo experiment and a series of comparison analyses using synthetic precipitation data. Our results show that the MKS fused precipitation performs better using the MO scheme than that using the ML scheme. Particularly, improvements are significant compared to that using the ML scheme for the fused precipitation associated with fine spatial resolutions. This is mainly due to having more criteria and constraints involved in the MO scheme than those included in the ML scheme. The weakness of the original ML scheme that blindly puts more weights onto the data associated with finer resolutions is overcome in our new approach.

High-resolution mapping of vehicle emissions in China in 2008

NASA Astrophysics Data System (ADS)

Zheng, B.; Huo, H.; Zhang, Q.; Yao, Z. L.; Wang, X. T.; Yang, X. F.; Liu, H.; He, K. B.

2014-09-01

This study is the first in a series of papers that aim to develop high-resolution emission databases for different anthropogenic sources in China. Here we focus on on-road transportation. Because of the increasing impact of on-road transportation on regional air quality, developing an accurate and high-resolution vehicle emission inventory is important for both the research community and air quality management. This work proposes a new inventory methodology to improve the spatial and temporal accuracy and resolution of vehicle emissions in China. We calculate, for the first time, the monthly vehicle emissions for 2008 in 2364 counties (an administrative unit one level lower than city) by developing a set of approaches to estimate vehicle stock and monthly emission factors at county-level, and technology distribution at provincial level. We then introduce allocation weights for the vehicle kilometers traveled to assign the county-level emissions onto 0.05° × 0.05° grids based on the China Digital Road-network Map (CDRM). The new methodology overcomes the common shortcomings of previous inventory methods, including neglecting the geographical differences between key parameters and using surrogates that are weakly related to vehicle activities to allocate vehicle emissions. The new method has great advantages over previous methods in depicting the spatial distribution characteristics of vehicle activities and emissions. This work provides a better understanding of the spatial representation of vehicle emissions in China and can benefit both air quality modeling and management with improved spatial accuracy.

High spatial resolution shortwave infrared imaging technology based on time delay and digital accumulation method

NASA Astrophysics Data System (ADS)

Jia, Jianxin; Wang, Yueming; Zhuang, Xiaoqiong; Yao, Yi; Wang, Shengwei; Zhao, Ding; Shu, Rong; Wang, Jianyu

2017-03-01

Shortwave infrared (SWIR) imaging technology attracts more and more attention by its fascinating ability of penetrating haze and smoke. For application of spaceborne remote sensing, spatial resolution of SWIR is lower compared with that of visible light (VIS) wavelength. It is difficult to balance between the spatial resolution and signal to noise ratio (SNR). Some conventional methods, such as enlarging aperture of telescope, image motion compensation, and analog time delay and integration (TDI) technology are used to gain SNR. These techniques bring in higher cost of satellite, complexity of system or other negative factors. In this paper, time delay and digital accumulation (TDDA) method is proposed to achieve higher spatial resolution. The method can enhance the SNR and non-uniformity of system theoretically. A prototype of SWIR imager consists of opto-mechanical, 1024 × 128 InGaAs detector, and electronics is designed and integrated to prove TDDA method. Both of measurements and experimental results indicate TDDA method can promote SNR of system approximated of the square root of accumulative stage. The results exhibit that non-uniformity of system is also improved by this approach to some extent. The experiment results are corresponded with the theoretical analysis. Based on the experiments results, it is proved firstly that the goal of 1 m ground sample distance (GSD) in orbit of 500 km is feasible with the TDDA stage of 30 for SWIR waveband (0.9-1.7 μm).

Optimal arrangements of fiber optic probes to enhance the spatial resolution in depth for 3D reflectance diffuse optical tomography with time-resolved measurements performed with fast-gated single-photon avalanche diodes

NASA Astrophysics Data System (ADS)

Puszka, Agathe; Di Sieno, Laura; Dalla Mora, Alberto; Pifferi, Antonio; Contini, Davide; Boso, Gianluca; Tosi, Alberto; Hervé, Lionel; Planat-Chrétien, Anne; Koenig, Anne; Dinten, Jean-Marc

2014-02-01

Fiber optic probes with a width limited to a few centimeters can enable diffuse optical tomography (DOT) in intern organs like the prostate or facilitate the measurements on extern organs like the breast or the brain. We have recently shown on 2D tomographic images that time-resolved measurements with a large dynamic range obtained with fast-gated single-photon avalanche diodes (SPADs) could push forward the imaged depth range in a diffusive medium at short source-detector separation compared with conventional non-gated approaches. In this work, we confirm these performances with the first 3D tomographic images reconstructed with such a setup and processed with the Mellin- Laplace transform. More precisely, we investigate the performance of hand-held probes with short interfiber distances in terms of spatial resolution and specifically demonstrate the interest of having a compact probe design featuring small source-detector separations. We compare the spatial resolution obtained with two probes having the same design but different scale factors, the first one featuring only interfiber distances of 15 mm and the second one, 10 mm. We evaluate experimentally the spatial resolution obtained with each probe on the setup with fast-gated SPADs for optical phantoms featuring two absorbing inclusions positioned at different depths and conclude on the potential of short source-detector separations for DOT.

Flexible hydrological modeling - Disaggregation from lumped catchment scale to higher spatial resolutions

NASA Astrophysics Data System (ADS)

Tran, Quoc Quan; Willems, Patrick; Pannemans, Bart; Blanckaert, Joris; Pereira, Fernando; Nossent, Jiri; Cauwenberghs, Kris; Vansteenkiste, Thomas

2015-04-01

Based on an international literature review on model structures of existing rainfall-runoff and hydrological models, a generalized model structure is proposed. It consists of different types of meteorological components, storage components, splitting components and routing components. They can be spatially organized in a lumped way, or on a grid, spatially interlinked by source-to-sink or grid-to-grid (cell-to-cell) routing. The grid size of the model can be chosen depending on the application. The user can select/change the spatial resolution depending on the needs and/or the evaluation of the accuracy of the model results, or use different spatial resolutions in parallel for different applications. Major research questions addressed during the study are: How can we assure consistent results of the model at any spatial detail? How can we avoid strong or sudden changes in model parameters and corresponding simulation results, when one moves from one level of spatial detail to another? How can we limit the problem of overparameterization/equifinality when we move from the lumped model to the spatially distributed model? The proposed approach is a step-wise one, where first the lumped conceptual model is calibrated using a systematic, data-based approach, followed by a disaggregation step where the lumped parameters are disaggregated based on spatial catchment characteristics (topography, land use, soil characteristics). In this way, disaggregation can be done down to any spatial scale, and consistently among scales. Only few additional calibration parameters are introduced to scale the absolute spatial differences in model parameters, but keeping the relative differences as obtained from the spatial catchment characteristics. After calibration of the spatial model, the accuracies of the lumped and spatial models were compared for peak, low and cumulative runoff total and sub-flows (at downstream and internal gauging stations). For the distributed models, additional validation on spatial results was done for the groundwater head values at observation wells. To ensure that the lumped model can produce results as accurate as the spatially distributed models or close regardless to the number of parameters and implemented physical processes, it was checked whether the structure of the lumped models had to be adjusted. The concept has been implemented in a PCRaster - Python platform and tested for two Belgian case studies (catchments of the rivers Dijle and Grote Nete). So far, use is made of existing model structures (NAM, PDM, VHM and HBV). Acknowledgement: These results were obtained within the scope of research activities for the Flemish Environment Agency (VMM) - division Operational Water Management on "Next Generation hydrological modeling", in cooperation with IMDC consultants, and for Flanders Hydraulics Research (Waterbouwkundig Laboratorium) on "Effect of climate change on the hydrological regime of navigable watercourses in Belgium".

Performance modeling of terahertz (THz) and millimeter waves (mmW) pupil plane imaging

NASA Astrophysics Data System (ADS)

Mohammadian, Nafiseh; Furxhi, Orges; Zhang, Lei; Offermans, Peter; Ghazi, Galia; Driggers, Ronald

2018-05-01

Terahertz- (THz) and millimeter-wave sensors are becoming more important in industrial, security, medical, and defense applications. A major problem in these sensing areas is the resolution, sensitivity, and visual acuity of the imaging systems. There are different fundamental parameters in designing a system that have significant effects on the imaging performance. The performance of THz systems can be discussed in terms of two characteristics: sensitivity and spatial resolution. New approaches for design and manufacturing of THz imagers are a vital basis for developing future applications. Photonics solutions have been at the technological forefront in THz band applications. A single scan antenna does not provide reasonable resolution, sensitivity, and speed. An effective approach to imaging is placing a high-performance antenna in a two-dimensional antenna array to achieve higher radiation efficiency and higher resolution in the imaging systems. Here, we present the performance modeling of a pupil plane imaging system to find the resolution and sensitivity efficiency of the imaging system.

Mapping Vegetation Community Types in a Highly-Disturbed Landscape: Integrating Hiearchical Object-Based Image Analysis with Digital Surface Models

NASA Astrophysics Data System (ADS)

Snavely, Rachel A.

Focusing on the semi-arid and highly disturbed landscape of San Clemente Island, California, this research tests the effectiveness of incorporating a hierarchal object-based image analysis (OBIA) approach with high-spatial resolution imagery and light detection and range (LiDAR) derived canopy height surfaces for mapping vegetation communities. The study is part of a large-scale research effort conducted by researchers at San Diego State University's (SDSU) Center for Earth Systems Analysis Research (CESAR) and Soil Ecology and Restoration Group (SERG), to develop an updated vegetation community map which will support both conservation and management decisions on Naval Auxiliary Landing Field (NALF) San Clemente Island. Trimble's eCognition Developer software was used to develop and generate vegetation community maps for two study sites, with and without vegetation height data as input. Overall and class-specific accuracies were calculated and compared across the two classifications. The highest overall accuracy (approximately 80%) was observed with the classification integrating airborne visible and near infrared imagery having very high spatial resolution with a LiDAR derived canopy height model. Accuracies for individual vegetation classes differed between both classification methods, but were highest when incorporating the LiDAR digital surface data. The addition of a canopy height model, however, yielded little difference in classification accuracies for areas of very dense shrub cover. Overall, the results show the utility of the OBIA approach for mapping vegetation with high spatial resolution imagery, and emphasizes the advantage of both multi-scale analysis and digital surface data for accuracy characterizing highly disturbed landscapes. The integrated imagery and digital canopy height model approach presented both advantages and limitations, which have to be considered prior to its operational use in mapping vegetation communities.

Detector motion method to increase spatial resolution in photon-counting detectors

NASA Astrophysics Data System (ADS)

Lee, Daehee; Park, Kyeongjin; Lim, Kyung Taek; Cho, Gyuseong

2017-03-01

Medical imaging requires high spatial resolution of an image to identify fine lesions. Photon-counting detectors in medical imaging have recently been rapidly replacing energy-integrating detectors due to the former`s high spatial resolution, high efficiency and low noise. Spatial resolution in a photon counting image is determined by the pixel size. Therefore, the smaller the pixel size, the higher the spatial resolution that can be obtained in an image. However, detector redesigning is required to reduce pixel size, and an expensive fine process is required to integrate a signal processing unit with reduced pixel size. Furthermore, as the pixel size decreases, charge sharing severely deteriorates spatial resolution. To increase spatial resolution, we propose a detector motion method using a large pixel detector that is less affected by charge sharing. To verify the proposed method, we utilized a UNO-XRI photon-counting detector (1-mm CdTe, Timepix chip) at the maximum X-ray tube voltage of 80 kVp. A similar spatial resolution of a 55- μm-pixel image was achieved by application of the proposed method to a 110- μm-pixel detector with a higher signal-to-noise ratio. The proposed method could be a way to increase spatial resolution without a pixel redesign when pixels severely suffer from charge sharing as pixel size is reduced.

Mentoring Temporal and Spatial Variations in Rainfall across Wadi Ar-Rumah, Saudi Arabia

NASA Astrophysics Data System (ADS)

Alharbi, T.; Ahmed, M.

2015-12-01

Across the Kingdom of Saudi Arabia (KSA), the fresh water resources are limited only to those found in aquifer systems. Those aquifers were believed to be recharged during the previous wet climatic period but still receiving modest local recharge in interleaving dry periods such as those prevailing at present. Quantifying temporal and spatial variabilities in rainfall patterns, magnitudes, durations, and frequencies is of prime importance when it comes to sustainable management of such aquifer systems. In this study, an integrated approach, using remote sensing and field data, was used to assess the past, the current, and the projected spatial and temporal variations in rainfall over one of the major watersheds in KSA, Wadi Ar-Rumah. This watershed was selected given its larger areal extent and population intensity. Rainfall data were extracted from (1) the Climate Prediction Centers (CPC) Merged Analysis of Precipitation (CMAP; spatial coverage: global; spatial resolution: 2.5° × 2.5°; temporal coverage: January 1979 to April 2015; temporal resolution: monthly), and (2) the Tropical Rainfall Measuring Mission (TRMM; spatial coverage: 50°N to 50°S; spatial resolution: 0.25° × 0.25°; temporal coverage: January 1998 to March 2015; temporal resolution: 3 hours) and calibrated against rainfall measurements extracted from rain gauges. Trends in rainfall patterns were examined over four main investigation periods: period I (01/1979 to 12/1985), period II (01/1986 to 12/1992), period III (01/1993 to 12/2002), and period IV (01/2003 to 12/2014). Our findings indicate: (1) a significant increase (+14.19 mm/yr) in rainfall rates were observed during period I, (2) a significant decrease in rainfall rates were observed during periods II (-5.80 mm/yr), III (-9.38 mm/yr), and IV (-2.46 mm/yr), and (3) the observed variations in rainfall rates are largely related to the temporal variations in the northerlies (also called northwesterlies) and the monsoonal wind regimes.

Future VIIRS enhancements for the integrated polar-orbiting environmental satellite system

NASA Astrophysics Data System (ADS)

Puschell, Jeffery J.; Silny, John; Cook, Lacy; Kim, Eugene

2010-08-01

The Visible/Infrared Imager Radiometer Suite (VIIRS) is the next-generation imaging spectroradiometer for the future operational polar-orbiting environmental satellite system. A successful Flight Unit 1 has been delivered and integrated onto the NPP spacecraft. The flexible VIIRS architecture can be adapted and enhanced to respond to a wide range of requirements and to incorporate new technology as it becomes available. This paper reports on recent design studies to evaluate building a MW-VLWIR dispersive hyperspectral module with active cooling into the existing VIIRS architecture. Performance of a two-grating VIIRS hyperspectral module was studied across a broad trade space defined primarily by spatial sampling, spectral range, spectral sampling interval, along-track field of view and integration time. The hyperspectral module studied here provides contiguous coverage across 3.9 - 15.5 μm with a spectral sampling interval of 10 nm or better, thereby extending VIIRS spectral range to the shortwave side of the 15.5 μm CO2 band and encompassing the 6.7 μm H2O band. Spatial sampling occurs at VIIRS I-band (~0.4 km at nadir) spatial resolution with aggregation to M-band (~0.8 km) and larger pixel sizes to improve sensitivity. Radiometric sensitivity (NEdT) at a spatial resolution of ~4 km is ~0.1 K or better for a 250 K scene across a wavelength range of 4.5 μm to 15.5 μm. The large number of high spectral and spatial resolution FOVs in this instrument improves chances for retrievals of information on the physical state and composition of the atmosphere all the way to the surface in cloudy regions relative to current systems. Spectral aggregation of spatial resolution measurements to MODIS and VIIRS multispectral bands would continue legacy measurements with better sensitivity in nearly all bands. Additional work is needed to optimize spatial sampling, spectral range and spectral sampling approaches for the hyperspectral module and to further refine this powerful imager concept.

High resolution modeling in urban hydrology: comparison between two modeling approaches and their sensitivity to high rainfall variability

NASA Astrophysics Data System (ADS)

Ichiba, Abdellah; Gires, Auguste; Tchiguirinskaia, Ioulia; Bompard, Philippe; Schertzer, Daniel

2015-04-01

Urban water management is becoming increasingly complex, due to the rapid increase of impervious areas, and the potential effects of climate change. The large amount of water generated in a very short period of time and the limited capacity of sewer systems increase the vulnerability of urban environments to flooding risk and make it necessary to implement specific devices in order to handle the volume of water generated. This complex situation in urban environments makes the use of hydrological models as well as the implementation of more accurate and reliable tools for flow and rainfall measurements essential for a good pluvial network management, the use of decision support tools such as real-time radar forecasting system, the developpement of general public communication and warning systems, and the implementation of management strategy participate on limiting the flood damages. The very high spatial variability characteristic of urban environments makes it necessary to integrate the variability of physical properties and precipitation at fine scales in modeling processes, suggesting a high resolution modeling approach. In this paper we suggest a comparison between two modeling approaches and their sensitivity to small-scale rainfall variability on a 2.15 km2 urban area located in the County of Val-de-Marne (South-East of Paris, France). The first model used in this study is CANOE, which is a semi-distributed model widely used in France by practitioners for urban hydrology and urban water management. Two configurations of this model are be used in this study, the first one integrate 9 sub-catchments with sizes range from (1ha to 76ha), in the second configuration, the spatial resolution of this model has been improved with 45 sub-catchments with sizes range from (1ha to 14ha), the aim is to see how the semi-distributed model resolution affects it sensitivity to rainfall variability. The second model is Multi-Hydro fully distributed model developed at the Ecole des Ponts ParisTech. It is an interacting core between open source software packages, each of them representing a portion of the water cycle in urban environment. Multi-Hydro has been set up at two resolutions, 10m and 5m. The validation of these two models is performed using 5 rainfall events that occurred between 2010 and 2013. Radar data comes from the Météo-France radar mosaic and the resolution is 1 km in space and 5 min in time. Raingauge and flow measurements data comes from the General Council of Val-de-Marne County. In this validation part, the hydrological responses given by two models and the different configurations are compared to flow measurements. It appears that CANOE gives better results than Multi-Hydro model, especially when using raingauge data. For some events, we noticed that model responses given when using raingauge and radar data are different, suggesting a sign of sensitivity to the spatial variability of rainfall. 10 high-resolution rainfall events are used in the second part to study the sensitivity of each modeling approach to high rainfall variability. Radar data was available at four spatial resolutions (100, 200, 500 and 1000m) and two temporal resolutions (1min and 5min), for each event, two rainfall directions (parallel and perpendicular) are used, meaning that 16 hydrological responses are simulated for each event and the variability within it analyzed. First results suggest that the fully distributed model is more sensitive to high rainfall variability than the semi-distributed one, the increase of both hydrological model spatial resolution improves their sensitivity to rainfall variability. This study highlights some technical challenges facing the high-resolution modeling, especially the difficulty to obtain reliable input data at an acceptable resolution and also the high computation time noticed particularly for the semi-distributed model making it difficult to use it in real time. The authors greatly acknowledge partial financial support from the project RainGain (http://www.raingain.eu) of the EU Interreg program.

The Effect of Remote Sensor Spatial Resolution in Monitoring U.S. Army Training Maneuver Sites

DTIC Science & Technology

1990-12-01

THE EFFECT OF REMOTE SENSOR SPATIAL RESOLUTION IN MONITORING U.S. ARMY...Multispectral Scanner with 6.5 meter spatial resolution provided the most effective digital data set for enhancing tank trails. However, this Airborne Scanner...primary objective of this research was to determine the capabilities and limitations of remote sensor systems having different spatial resolutions to

Assessing resolution in live cell structured illumination microscopy

NASA Astrophysics Data System (ADS)

Pospíšil, Jakub; Fliegel, Karel; Klíma, Miloš

2017-12-01

Structured Illumination Microscopy (SIM) is a powerful super-resolution technique, which is able to enhance the resolution of optical microscope beyond the Abbe diffraction limit. In the last decade, numerous SIM methods that achieve the resolution of 100 nm in the lateral dimension have been developed. The SIM setups with new high-speed cameras and illumination pattern generators allow rapid acquisition of the live specimen. Therefore, SIM is widely used for investigation of the live structures in molecular and live cell biology. Quantitative evaluation of resolution enhancement in a real sample is essential to describe the efficiency of super-resolution microscopy technique. However, measuring the resolution of a live cell sample is a challenging task. Based on our experimental findings, the widely used Fourier ring correlation (FRC) method does not seem to be well suited for measuring the resolution of SIM live cell video sequences. Therefore, the resolution assessing methods based on Fourier spectrum analysis are often used. We introduce a measure based on circular average power spectral density (PSDca) estimated from a single SIM image (one video frame). PSDca describes the distribution of the power of a signal with respect to its spatial frequency. Spatial resolution corresponds to the cut-off frequency in Fourier space. In order to estimate the cut-off frequency from a noisy signal, we use a spectral subtraction method for noise suppression. In the future, this resolution assessment approach might prove useful also for single-molecule localization microscopy (SMLM) live cell imaging.

Assessing the performance of multiple spectral-spatial features of a hyperspectral image for classification of urban land cover classes using support vector machines and artificial neural network

NASA Astrophysics Data System (ADS)

Pullanagari, Reddy; Kereszturi, Gábor; Yule, Ian J.; Ghamisi, Pedram

2017-04-01

Accurate and spatially detailed mapping of complex urban environments is essential for land managers. Classifying high spectral and spatial resolution hyperspectral images is a challenging task because of its data abundance and computational complexity. Approaches with a combination of spectral and spatial information in a single classification framework have attracted special attention because of their potential to improve the classification accuracy. We extracted multiple features from spectral and spatial domains of hyperspectral images and evaluated them with two supervised classification algorithms; support vector machines (SVM) and an artificial neural network. The spatial features considered are produced by a gray level co-occurrence matrix and extended multiattribute profiles. All of these features were stacked, and the most informative features were selected using a genetic algorithm-based SVM. After selecting the most informative features, the classification model was integrated with a segmentation map derived using a hidden Markov random field. We tested the proposed method on a real application of a hyperspectral image acquired from AisaFENIX and on widely used hyperspectral images. From the results, it can be concluded that the proposed framework significantly improves the results with different spectral and spatial resolutions over different instrumentation.

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution.

PubMed

Gao, Qi; Zribi, Mehrez; Escorihuela, Maria Jose; Baghdadi, Nicolas

2017-08-26

The recent deployment of ESA's Sentinel operational satellites has established a new paradigm for remote sensing applications. In this context, Sentinel-1 radar images have made it possible to retrieve surface soil moisture with a high spatial and temporal resolution. This paper presents two methodologies for the retrieval of soil moisture from remotely-sensed SAR images, with a spatial resolution of 100 m. These algorithms are based on the interpretation of Sentinel-1 data recorded in the VV polarization, which is combined with Sentinel-2 optical data for the analysis of vegetation effects over a site in Urgell (Catalunya, Spain). The first algorithm has already been applied to observations in West Africa by Zribi et al., 2008, using low spatial resolution ERS scatterometer data, and is based on change detection approach. In the present study, this approach is applied to Sentinel-1 data and optimizes the inversion process by taking advantage of the high repeat frequency of the Sentinel observations. The second algorithm relies on a new method, based on the difference between backscattered Sentinel-1 radar signals observed on two consecutive days, expressed as a function of NDVI optical index. Both methods are applied to almost 1.5 years of satellite data (July 2015-November 2016), and are validated using field data acquired at a study site. This leads to an RMS error in volumetric moisture of approximately 0.087 m³/m³ and 0.059 m³/m³ for the first and second methods, respectively. No site calibrations are needed with these techniques, and they can be applied to any vegetation-covered area for which time series of SAR data have been recorded.

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution

PubMed Central

Gao, Qi; Zribi, Mehrez

2017-01-01

The recent deployment of ESA’s Sentinel operational satellites has established a new paradigm for remote sensing applications. In this context, Sentinel-1 radar images have made it possible to retrieve surface soil moisture with a high spatial and temporal resolution. This paper presents two methodologies for the retrieval of soil moisture from remotely-sensed SAR images, with a spatial resolution of 100 m. These algorithms are based on the interpretation of Sentinel-1 data recorded in the VV polarization, which is combined with Sentinel-2 optical data for the analysis of vegetation effects over a site in Urgell (Catalunya, Spain). The first algorithm has already been applied to observations in West Africa by Zribi et al., 2008, using low spatial resolution ERS scatterometer data, and is based on change detection approach. In the present study, this approach is applied to Sentinel-1 data and optimizes the inversion process by taking advantage of the high repeat frequency of the Sentinel observations. The second algorithm relies on a new method, based on the difference between backscattered Sentinel-1 radar signals observed on two consecutive days, expressed as a function of NDVI optical index. Both methods are applied to almost 1.5 years of satellite data (July 2015–November 2016), and are validated using field data acquired at a study site. This leads to an RMS error in volumetric moisture of approximately 0.087 m3/m3 and 0.059 m3/m3 for the first and second methods, respectively. No site calibrations are needed with these techniques, and they can be applied to any vegetation-covered area for which time series of SAR data have been recorded. PMID:28846601

Porous media flux sensitivity to pore-scale geostatistics: A bottom-up approach

NASA Astrophysics Data System (ADS)

Di Palma, P. R.; Guyennon, N.; Heße, F.; Romano, E.

2017-04-01

Macroscopic properties of flow through porous media can be directly computed by solving the Navier-Stokes equations at the scales related to the actual flow processes, while considering the porous structures in an explicit way. The aim of this paper is to investigate the effects of the pore-scale spatial distribution on seepage velocity through numerical simulations of 3D fluid flow performed by the lattice Boltzmann method. To this end, we generate multiple random Gaussian fields whose spatial correlation follows an assigned semi-variogram function. The Exponential and Gaussian semi-variograms are chosen as extreme-cases of correlation for short distances and statistical properties of the resulting porous media (indicator field) are described using the Matèrn covariance model, with characteristic lengths of spatial autocorrelation (pore size) varying from 2% to 13% of the linear domain. To consider the sensitivity of the modeling results to the geostatistical representativeness of the domain as well as to the adopted resolution, porous media have been generated repetitively with re-initialized random seeds and three different resolutions have been tested for each resulting realization. The main difference among results is observed between the two adopted semi-variograms, indicating that the roughness (short distances autocorrelation) is the property mainly affecting the flux. However, computed seepage velocities show additionally a wide variability (about three orders of magnitude) for each semi-variogram model in relation to the assigned correlation length, corresponding to pore sizes. The spatial resolution affects more the results for short correlation lengths (i.e., small pore sizes), resulting in an increasing underestimation of the seepage velocity with the decreasing correlation length. On the other hand, results show an increasing uncertainty as the correlation length approaches the domain size.

Climate-based archetypes for the environmental fate assessment of chemicals.

PubMed

Ciuffo, Biagio; Sala, Serenella

2013-11-15

Emissions of chemicals have been on the rise for years, and their impacts are greatly influenced by spatial differentiation. Chemicals are usually emitted locally but their impact can be felt both locally and globally, due to their chemical properties and persistence. The variability of environmental parameters in the emission compartment may affect the chemicals' fate and the exposure at different orders of magnitude. The assessment of the environmental fate of chemicals and the inherent spatial differentiation requires the use of multimedia models at various levels of complexity (from a simple box model to complex computational and high-spatial-resolution models). The objective of these models is to support ecological and human health risk assessment, by reducing the uncertainty of chemical impact assessments. The parameterisation of spatially resolved multimedia models is usually based on scenarios of evaluative environments, or on geographical resolutions related to administrative boundaries (e.g. countries/continents) or landscape areas (e.g. watersheds, eco-regions). The choice of the most appropriate scale and scenario is important from a management perspective, as a balance should be reached between a simplified approach and computationally intensive multimedia models. In this paper, which aims to go beyond the more traditional approach based on scale/resolution (cell, country, and basin), we propose and assess climate-based archetypes for the impact assessment of chemicals released in air. We define the archetypes based on the main drivers of spatial variability, which we systematically identify by adopting global sensitivity analysis techniques. A case study that uses the high resolution multimedia model MAPPE (Multimedia Assessment of Pollutant Pathways in the Environment) is presented. Results of the analysis showed that suitable archetypes should be both climate- and chemical-specific, as different chemicals (or groups of them) have different traits that influence their spatial variability. This hypothesis was tested by comparing the variability of the output of MAPPE for four different climatic zones on four different continents for four different chemicals (which represent different combinations of physical and chemical properties). Results showed the high suitability of climate-based archetypes in assessing the impacts of chemicals released in air. However, further research work is still necessary to test these findings. Copyright © 2013 Elsevier Ltd. All rights reserved.

Monitoring the dynamics of surface water fraction from MODIS time series in a Mediterranean environment

NASA Astrophysics Data System (ADS)

Li, Linlin; Vrieling, Anton; Skidmore, Andrew; Wang, Tiejun; Turak, Eren

2018-04-01

Detailed spatial information of changes in surface water extent is needed for water management and biodiversity conservation, particularly in drier parts of the globe where small, temporally-variant wetlands prevail. Although global surface water histories are now generated from 30 m Landsat data, for many locations they contain large temporal gaps particularly for longer periods (>10 years) due to revisit intervals and cloud cover. Daily Moderate Resolution Imaging Spectrometer (MODIS) imagery has potential to fill such gaps, but its relatively coarse spatial resolution may not detect small water bodies, which can be of great ecological importance. To address this problem, this study proposes and tests options for estimating the surface water fraction from MODIS 16-day 500 m Bidirectional Reflectance Distribution Function (BRDF) corrected surface reflectance image composites. The spatial extent of two Landsat tiles over Spain were selected as test areas. We obtained a 500 m reference dataset on surface water fraction by spatially aggregating 30 m binary water masks obtained from the Landsat-derived C-version of Function of Mask (CFmask), which themselves were evaluated against high-resolution Google Earth imagery. Twelve regression tree models were developed with two approaches, Random Forest and Cubist, using spectral metrics derived from MODIS data and topographic parameters generated from a 30 m spatial resolution digital elevation model. Results showed that accuracies were higher when we included annual summary statistics of the spectral metrics as predictor variables. Models trained on a single Landsat tile were ineffective in mapping surface water in the other tile, but global models trained with environmental conditions from both tiles can provide accurate results for both study areas. We achieved the highest accuracy with Cubist global model (R2 = 0.91, RMSE = 11.05%, MAE = 7.67%). Our method was not only effective for mapping permanent water fraction, but also in accurately capturing temporal fluctuations of surface water. Based on this good performance, we produced surface water fraction maps at 16-day interval for the 2000-2015 MODIS archive. Our approach is promising for monitoring surface water fraction at high frequency time intervals over much larger regions provided that training data are collected across the spatial domain for which the model will be applied.

The effects of transient attention on spatial resolution and the size of the attentional cue.

PubMed

Yeshurun, Yaffa; Carrasco, Marisa

2008-01-01

It has been shown that transient attention enhances spatial resolution, but is the effect of transient attention on spatial resolution modulated by the size of the attentional cue? Would a gradual increase in the size of the cue lead to a gradual decrement in spatial resolution? To test these hypotheses, we used a texture segmentation task in which performance depends on spatial resolution, and systematically manipulated the size of the attentional cue: A bar of different lengths (Experiment 1) or a frame of different sizes (Experiments 2-3) indicated the target region in a texture segmentation display. Observers indicated whether a target patch region (oriented line elements in a background of an orthogonal orientation), appearing at a range of eccentricities, was present in the first or the second interval. We replicated the attentional enhancement of spatial resolution found with small cues; attention improved performance at peripheral locations but impaired performance at central locations. However, there was no evidence of gradual resolution decrement with large cues. Transient attention enhanced spatial resolution at the attended location when it was attracted to that location by a small cue but did not affect resolution when it was attracted by a large cue. These results indicate that transient attention cannot adapt its operation on spatial resolution on the basis of the size of the attentional cue.

Selecting a spatial resolution for estimation of per-field green leaf area index

NASA Technical Reports Server (NTRS)

Curran, Paul J.; Williamson, H. Dawn

1988-01-01

For any application of multispectral scanner (MSS) data, a user is faced with a number of choices concerning the characteristics of the data; one of these is their spatial resolution. A pilot study was undertaken to determine the spatial resolution that would be optimal for the per-field estimation of green leaf area index (GLAI) in grassland. By reference to empirically-derived data from three areas of grassland, the suitable spatial resolution was hypothesized to lie in the lower portion of a 2-18 m range. To estimate per-field GLAI, airborne MSS data were collected at spatial resolutions of 2 m, 5 m and 10 m. The highest accuracies of per-field GLAI estimation were achieved using MSS data with spatial resolutions of 2 m and 5 m.

The impact of physical navigation on spatial organization for sensemaking.

PubMed

Andrews, Christopher; North, Chris

2013-12-01

Spatial organization has been proposed as a compelling approach to externalizing the sensemaking process. However, there are two ways in which space can be provided to the user: by creating a physical workspace that the user can interact with directly, such as can be provided by a large, high-resolution display, or through the use of a virtual workspace that the user navigates using virtual navigation techniques such as zoom and pan. In this study we explicitly examined the use of spatial sensemaking techniques within these two environments. The results demonstrate that these two approaches to providing sensemaking space are not equivalent, and that the greater embodiment afforded by the physical workspace changes how the space is perceived and used, leading to increased externalization of the sensemaking process.

Benthic Habitat Mapping Using Multispectral High-Resolution Imagery: Evaluation of Shallow Water Atmospheric Correction Techniques.

PubMed

Eugenio, Francisco; Marcello, Javier; Martin, Javier; Rodríguez-Esparragón, Dionisio

2017-11-16

Remote multispectral data can provide valuable information for monitoring coastal water ecosystems. Specifically, high-resolution satellite-based imaging systems, as WorldView-2 (WV-2), can generate information at spatial scales needed to implement conservation actions for protected littoral zones. However, coastal water-leaving radiance arriving at the space-based sensor is often small as compared to reflected radiance. In this work, complex approaches, which usually use an accurate radiative transfer code to correct the atmospheric effects, such as FLAASH, ATCOR and 6S, have been implemented for high-resolution imagery. They have been assessed in real scenarios using field spectroradiometer data. In this context, the three approaches have achieved excellent results and a slightly superior performance of 6S model-based algorithm has been observed. Finally, for the mapping of benthic habitats in shallow-waters marine protected environments, a relevant application of the proposed atmospheric correction combined with an automatic deglinting procedure is presented. This approach is based on the integration of a linear mixing model of benthic classes within the radiative transfer model of the water. The complete methodology has been applied to selected ecosystems in the Canary Islands (Spain) but the obtained results allow the robust mapping of the spatial distribution and density of seagrass in coastal waters and the analysis of multitemporal variations related to the human activity and climate change in littoral zones.

Benthic Habitat Mapping Using Multispectral High-Resolution Imagery: Evaluation of Shallow Water Atmospheric Correction Techniques

PubMed Central

Eugenio, Francisco; Marcello, Javier; Martin, Javier

2017-01-01

Remote multispectral data can provide valuable information for monitoring coastal water ecosystems. Specifically, high-resolution satellite-based imaging systems, as WorldView-2 (WV-2), can generate information at spatial scales needed to implement conservation actions for protected littoral zones. However, coastal water-leaving radiance arriving at the space-based sensor is often small as compared to reflected radiance. In this work, complex approaches, which usually use an accurate radiative transfer code to correct the atmospheric effects, such as FLAASH, ATCOR and 6S, have been implemented for high-resolution imagery. They have been assessed in real scenarios using field spectroradiometer data. In this context, the three approaches have achieved excellent results and a slightly superior performance of 6S model-based algorithm has been observed. Finally, for the mapping of benthic habitats in shallow-waters marine protected environments, a relevant application of the proposed atmospheric correction combined with an automatic deglinting procedure is presented. This approach is based on the integration of a linear mixing model of benthic classes within the radiative transfer model of the water. The complete methodology has been applied to selected ecosystems in the Canary Islands (Spain) but the obtained results allow the robust mapping of the spatial distribution and density of seagrass in coastal waters and the analysis of multitemporal variations related to the human activity and climate change in littoral zones. PMID:29144444

Hyperpolarized (129) Xe imaging of the rat lung using spiral IDEAL.

PubMed

Doganay, Ozkan; Wade, Trevor; Hegarty, Elaine; McKenzie, Charles; Schulte, Rolf F; Santyr, Giles E

2016-08-01

To implement and optimize a single-shot spiral encoding strategy for rapid 2D IDEAL projection imaging of hyperpolarized (Hp) (129) Xe in the gas phase, and in the pulmonary tissue (PT) and red blood cells (RBCs) compartments of the rat lung, respectively. A theoretical and experimental point spread function analysis was used to optimize the spiral k-space read-out time in a phantom. Hp (129) Xe IDEAL images from five healthy rats were used to: (i) optimize flip angles by a Bloch equation analysis using measured kinetics of gas exchange and (ii) investigate the feasibility of the approach to characterize the exchange of Hp (129) Xe. A read-out time equal to approximately 1.8 × T2* was found to provide the best trade-off between spatial resolution and signal-to-noise ratio (SNR). Spiral IDEAL approaches that use the entire dissolved phase magnetization should give an SNR improvement of a factor of approximately three compared with Cartesian approaches with similar spatial resolution. The IDEAL strategy allowed imaging of gas, PT, and RBC compartments with sufficient SNR and temporal resolution to permit regional gas exchange measurements in healthy rats. Single-shot spiral IDEAL imaging of gas, PT and RBC compartments and gas exchange is feasible in rat lung using Hp (129) Xe. Magn Reson Med 76:566-576, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Index mismatch aberration correction over long working distances using spatial light modulation.

PubMed

Gjonaj, Bergin; Johnson, Patrick; Bonn, Mischa; Domke, Katrin F

2012-11-20

For many microscopy applications, millimeters-long free working distances (LWD) are required. However, the high resolution and contrast of LWD objectives operated in air are lost when introducing glass and/or liquid with the sample. We propose to use spatial light modulation to correct for such beam aberrations caused by refractive index mismatches. Focusing a monochromatic laser beam with a 10 mm working distance air objective (50×, 0.5 NA) through air, glass, and water, we manage to restore a sharp, intense focus (FWHM<2λ) by adaptive beam phase shaping. Our approach offers a practical and cost-effective route to high resolution and contrast microscopy using LWD air objectives, extending their usage beyond applications in air.

Fast backprojection-based reconstruction of spectral-spatial EPR images from projections with the constant sweep of a magnetic field.

PubMed

Komarov, Denis A; Hirata, Hiroshi

2017-08-01

In this paper, we introduce a procedure for the reconstruction of spectral-spatial EPR images using projections acquired with the constant sweep of a magnetic field. The application of a constant field-sweep and a predetermined data sampling rate simplifies the requirements for EPR imaging instrumentation and facilitates the backprojection-based reconstruction of spectral-spatial images. The proposed approach was applied to the reconstruction of a four-dimensional numerical phantom and to actual spectral-spatial EPR measurements. Image reconstruction using projections with a constant field-sweep was three times faster than the conventional approach with the application of a pseudo-angle and a scan range that depends on the applied field gradient. Spectral-spatial EPR imaging with a constant field-sweep for data acquisition only slightly reduces the signal-to-noise ratio or functional resolution of the resultant images and can be applied together with any common backprojection-based reconstruction algorithm. Copyright © 2017 Elsevier Inc. All rights reserved.

Optimization of the launcher ascent trajectory leading to the global optimum without any initialization: the breakthrough of the Hamilton-Jacobi-Bellman approach

NASA Astrophysics Data System (ADS)

Bourgeois, E.; Bokanowski, O.; Zidani, H.; Désilles, A.

2018-06-01

The resolution of the launcher ascent trajectory problem by the so-called Hamilton-Jacobi-Bellman (HJB) approach, relying on the Dynamic Programming Principle, has been investigated. The method gives a global optimum and does not need any initialization procedure. Despite these advantages, this approach is seldom used because of the dicculties of computing the solution of the HJB equation for high dimension problems. The present study shows that an eccient resolution is found. An illustration of the method is proposed on a heavy class launcher, for a typical GEO (Geostationary Earth Orbit) mission. This study has been performed in the frame of the Centre National d'Etudes Spatiales (CNES) Launchers Research & Technology Program.

A Phenology-based Method For Identifying the Planting Fraction of Winter Wheat Using Moderate-resolution Satellite Data

NASA Astrophysics Data System (ADS)

Dong, J.; Liu, W.; Han, W.; Lei, T.; Xia, J.; Yuan, W.

2017-12-01

Winter wheat is a staple food crop for most of the world's population, and the area and spatial distribution of winter wheat are key elements in estimating crop production and ensuring food security. However, winter wheat planting areas contain substantial spatial heterogeneity with mixed pixels for coarse- and moderate-resolution satellite data, leading to significant errors in crop acreage estimation. This study has developed a phenology-based approach using moderate-resolution satellite data to estimate sub-pixel planting fractions of winter wheat. Based on unmanned aerial vehicle (UAV) observations, the unique characteristics of winter wheat with high vegetation index values at the heading stage (May) and low values at the harvest stage (June) were investigated. The differences in vegetation index between heading and harvest stages increased with the planting fraction of winter wheat, and therefore the planting fractions were estimated by comparing the NDVI differences of a given pixel with those of predetermined pure winter wheat and non-winter wheat pixels. This approach was evaluated using aerial images and agricultural statistical data in an intensive agricultural region, Shandong Province in North China. The method explained 60% and 85% of the spatial variation in county- and municipal-level statistical data, respectively. More importantly, the predetermined pure winter wheat and non-winter wheat pixels can be automatically identified using MODIS data according to their NDVI differences, which strengthens the potential to use this method at regional and global scales without any field observations as references.

Estimation of Global 1km-grid Terrestrial Carbon Exchange Part I: Developing Inputs and Modelling

NASA Astrophysics Data System (ADS)

Sasai, T.; Murakami, K.; Kato, S.; Matsunaga, T.; Saigusa, N.; Hiraki, K.

2015-12-01

Global terrestrial carbon cycle largely depends on a spatial pattern in land cover type, which is heterogeneously-distributed over regional and global scales. However, most studies, which aimed at the estimation of carbon exchanges between ecosystem and atmosphere, remained within several tens of kilometers grid spatial resolution, and the results have not been enough to understand the detailed pattern of carbon exchanges based on ecological community. Improving the sophistication of spatial resolution is obviously necessary to enhance the accuracy of carbon exchanges. Moreover, the improvement may contribute to global warming awareness, policy makers and other social activities. In this study, we show global terrestrial carbon exchanges (net ecosystem production, net primary production, and gross primary production) with 1km-grid resolution. As methodology for computing the exchanges, we 1) developed a global 1km-grid climate and satellite dataset based on the approach in Setoyama and Sasai (2013); 2) used the satellite-driven biosphere model (Biosphere model integrating Eco-physiological And Mechanistic approaches using Satellite data: BEAMS) (Sasai et al., 2005, 2007, 2011); 3) simulated the carbon exchanges by using the new dataset and BEAMS by the use of a supercomputer that includes 1280 CPU and 320 GPGPU cores (GOSAT RCF of NIES). As a result, we could develop a global uniform system for realistically estimating terrestrial carbon exchange, and evaluate net ecosystem production in each community level; leading to obtain highly detailed understanding of terrestrial carbon exchanges.

Approach to simultaneously denoise and invert backscatter and extinction from photon-limited atmospheric lidar observations.

PubMed

Marais, Willem J; Holz, Robert E; Hu, Yu Hen; Kuehn, Ralph E; Eloranta, Edwin E; Willett, Rebecca M

2016-10-10

Atmospheric lidar observations provide a unique capability to directly observe the vertical column of cloud and aerosol scattering properties. Detector and solar-background noise, however, hinder the ability of lidar systems to provide reliable backscatter and extinction cross-section estimates. Standard methods for solving this inverse problem are most effective with high signal-to-noise ratio observations that are only available at low resolution in uniform scenes. This paper describes a novel method for solving the inverse problem with high-resolution, lower signal-to-noise ratio observations that are effective in non-uniform scenes. The novelty is twofold. First, the inferences of the backscatter and extinction are applied to images, whereas current lidar algorithms only use the information content of single profiles. Hence, the latent spatial and temporal information in noisy images are utilized to infer the cross-sections. Second, the noise associated with photon-counting lidar observations can be modeled using a Poisson distribution, and state-of-the-art tools for solving Poisson inverse problems are adapted to the atmospheric lidar problem. It is demonstrated through photon-counting high spectral resolution lidar (HSRL) simulations that the proposed algorithm yields inverted backscatter and extinction cross-sections (per unit volume) with smaller mean squared error values at higher spatial and temporal resolutions, compared to the standard approach. Two case studies of real experimental data are also provided where the proposed algorithm is applied on HSRL observations and the inverted backscatter and extinction cross-sections are compared against the standard approach.

Forest Classification Accuracy as Influenced by Multispectral Scanner Spatial Resolution. [Sam Houston National Forest, Texas

NASA Technical Reports Server (NTRS)

Nalepka, R. F. (Principal Investigator); Sadowski, F. E.; Sarno, J. E.

1976-01-01

The author has identified the following significant results. A supervised classification within two separate ground areas of the Sam Houston National Forest was carried out for two sq meters spatial resolution MSS data. Data were progressively coarsened to simulate five additional cases of spatial resolution ranging up to 64 sq meters. Similar processing and analysis of all spatial resolutions enabled evaluations of the effect of spatial resolution on classification accuracy for various levels of detail and the effects on area proportion estimation for very general forest features. For very coarse resolutions, a subset of spectral channels which simulated the proposed thematic mapper channels was used to study classification accuracy.

A Voronoi interior adjacency-based approach for generating a contour tree

NASA Astrophysics Data System (ADS)

Chen, Jun; Qiao, Chaofei; Zhao, Renliang

2004-05-01

A contour tree is a good graphical tool for representing the spatial relations of contour lines and has found many applications in map generalization, map annotation, terrain analysis, etc. A new approach for generating contour trees by introducing a Voronoi-based interior adjacency set concept is proposed in this paper. The immediate interior adjacency set is employed to identify all of the children contours of each contour without contour elevations. It has advantages over existing methods such as the point-in-polygon method and the region growing-based method. This new approach can be used for spatial data mining and knowledge discovering, such as the automatic extraction of terrain features and construction of multi-resolution digital elevation model.

Exposure prediction approaches used in air pollution epidemiology studies: Keyfindings and future recommendations

EPA Science Inventory

Many epidemiologic studies of the health effects of exposure to ambient air pollution use measurements from central-site monitors as their exposure estimate. However, measurements from central-site monitors may lack the spatial and temporal resolution required to capture exposure...

Histogram Curve Matching Approaches for Object-based Image Classification of Land Cover and Land Use

PubMed Central

Toure, Sory I.; Stow, Douglas A.; Weeks, John R.; Kumar, Sunil

2013-01-01

The classification of image-objects is usually done using parametric statistical measures of central tendency and/or dispersion (e.g., mean or standard deviation). The objectives of this study were to analyze digital number histograms of image objects and evaluate classifications measures exploiting characteristic signatures of such histograms. Two histograms matching classifiers were evaluated and compared to the standard nearest neighbor to mean classifier. An ADS40 airborne multispectral image of San Diego, California was used for assessing the utility of curve matching classifiers in a geographic object-based image analysis (GEOBIA) approach. The classifications were performed with data sets having 0.5 m, 2.5 m, and 5 m spatial resolutions. Results show that histograms are reliable features for characterizing classes. Also, both histogram matching classifiers consistently performed better than the one based on the standard nearest neighbor to mean rule. The highest classification accuracies were produced with images having 2.5 m spatial resolution. PMID:24403648

Spatial resolution enhancement of satellite image data using fusion approach

NASA Astrophysics Data System (ADS)

Lestiana, H.; Sukristiyanti

2018-02-01

Object identification using remote sensing data has a problem when the spatial resolution is not in accordance with the object. The fusion approach is one of methods to solve the problem, to improve the object recognition and to increase the objects information by combining data from multiple sensors. The application of fusion image can be used to estimate the environmental component that is needed to monitor in multiple views, such as evapotranspiration estimation, 3D ground-based characterisation, smart city application, urban environments, terrestrial mapping, and water vegetation. Based on fusion application method, the visible object in land area has been easily recognized using the method. The variety of object information in land area has increased the variation of environmental component estimation. The difficulties in recognizing the invisible object like Submarine Groundwater Discharge (SGD), especially in tropical area, might be decreased by the fusion method. The less variation of the object in the sea surface temperature is a challenge to be solved.

Investigating Bacterial-Animal Symbioses with Light Sheet Microscopy

PubMed Central

Taormina, Michael J.; Jemielita, Matthew; Stephens, W. Zac; Burns, Adam R.; Troll, Joshua V.; Parthasarathy, Raghuveer; Guillemin, Karen

2014-01-01

SUMMARY Microbial colonization of the digestive tract is a crucial event in vertebrate development, required for maturation of host immunity and establishment of normal digestive physiology. Advances in genomic, proteomic, and metabolomic technologies are providing a more detailed picture of the constituents of the intestinal habitat, but these approaches lack the spatial and temporal resolution needed to characterize the assembly and dynamics of microbial communities in this complex environment. We report the use of light sheet microscopy to provide high resolution imaging of bacterial colonization of the zebrafish intestine. The methodology allows us to characterize bacterial population dynamics across the entire organ and the behaviors of individual bacterial and host cells throughout the colonization process. The large four-dimensional datasets generated by these imaging approaches require new strategies for image analysis. When integrated with other “omics” datasets, information about the spatial and temporal dynamics of microbial cells within the vertebrate intestine will provide new mechanistic insights into how microbial communities assemble and function within hosts. PMID:22983029

Improved Technology To Prevent Nuclear Proliferation And Counter Nuclear Terrorism

DOE Office of Scientific and Technical Information (OSTI.GOV)

Richardson, J; Yuldashev, B; Labov, S

2006-06-12

As the world moves into the 21st century, the possibility of greater reliance on nuclear energy will impose additional technical requirements to prevent proliferation. In addition to proliferation resistant reactors, a careful examination of the various possible fuel cycles from cradle to grave will provide additional technical and nonproliferation challenges in the areas of conversion, enrichment, transportation, recycling and waste disposal. Radiation detection technology and information management have a prominent role in any future global regime for nonproliferation. As nuclear energy and hence nuclear materials become an increasingly global phenomenon, using local technologies and capabilities facilitate incorporation of enhanced monitoringmore » and detection on the regional level. Radiation detection technologies are an important tool in the prevention of proliferation and countering radiological/nuclear terrorism. A variety of new developments have enabled enhanced performance in terms of energy resolution, spatial resolution, passive detection, predictive modeling and simulation, active interrogation, and ease of operation and deployment in the field. For example, various gamma ray imaging approaches are being explored to combine spatial resolution with background suppression in order to enhance sensitivity many-fold at reasonable standoff distances and acquisition times. New materials and approaches are being developed in order to provide adequate energy resolution in field use without the necessity for liquid nitrogen. Different detection algorithms enable fissile materials to be distinguished from other radioisotopes.« less

Adaptive windowing in contrast-enhanced intravascular ultrasound imaging.

PubMed

Lindsey, Brooks D; Martin, K Heath; Jiang, Xiaoning; Dayton, Paul A

2016-08-01

Intravascular ultrasound (IVUS) is one of the most commonly-used interventional imaging techniques and has seen recent innovations which attempt to characterize the risk posed by atherosclerotic plaques. One such development is the use of microbubble contrast agents to image vasa vasorum, fine vessels which supply oxygen and nutrients to the walls of coronary arteries and typically have diameters less than 200μm. The degree of vasa vasorum neovascularization within plaques is positively correlated with plaque vulnerability. Having recently presented a prototype dual-frequency transducer for contrast agent-specific intravascular imaging, here we describe signal processing approaches based on minimum variance (MV) beamforming and the phase coherence factor (PCF) for improving the spatial resolution and contrast-to-tissue ratio (CTR) in IVUS imaging. These approaches are examined through simulations, phantom studies, ex vivo studies in porcine arteries, and in vivo studies in chicken embryos. In phantom studies, PCF processing improved CTR by a mean of 4.2dB, while combined MV and PCF processing improved spatial resolution by 41.7%. Improvements of 2.2dB in CTR and 37.2% in resolution were observed in vivo. Applying these processing strategies can enhance image quality in conventional B-mode IVUS or in contrast-enhanced IVUS, where signal-to-noise ratio is relatively low and resolution is at a premium. Copyright © 2016 Elsevier B.V. All rights reserved.

Assessing the response of area burned to changing climate in western boreal North America using a Multivariate Adaptive Regression Splines (MARS) approach

Treesearch

Michael S. Balshi; A. David McGuire; Paul Duffy; Mike Flannigan; John Walsh; Jerry Melillo

2009-01-01

We developed temporally and spatially explicit relationships between air temperature and fuel moisture codes derived from the Canadian Fire Weather Index System to estimate annual area burned at 2.5o (latitude x longitude) resolution using a Multivariate Adaptive Regression Spline (MARS) approach across Alaska and Canada. Burned area was...

A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data

Treesearch

B. Tyler Wilson; Andrew J. Lister; Rachel I. Riemann

2012-01-01

The paper describes an efficient approach for mapping multiple individual tree species over large spatial domains. The method integrates vegetation phenology derived from MODIS imagery and raster data describing relevant environmental parameters with extensive field plot data of tree species basal area to create maps of tree species abundance and distribution at a 250-...

Quantitative phase imaging using a programmable wavefront sensor

NASA Astrophysics Data System (ADS)

Soldevila, F.; Durán, V.; Clemente, P.; Lancis, J.; Tajahuerce, E.

2018-02-01

We perform phase imaging using a non-interferometric approach to measure the complex amplitude of a wavefront. We overcome the limitations in spatial resolution, optical efficiency, and dynamic range that are found in Shack-Hartmann wavefront sensing. To do so, we sample the wavefront with a high-speed spatial light modulator. A single lens forms a time-dependent light distribution on its focal plane, where a position detector is placed. Our approach is lenslet-free and does not rely on any kind of iterative or unwrap algorithm. The validity of our technique is demonstrated by performing both aberration sensing and phase imaging of transparent samples.

High Resolution Mesoscale Weather Data Improvement to Spatial Effects for Dose-Rate Contour Plot Predictions

DTIC Science & Technology

2007-03-01

time. This is a very powerful tool in determining fine spatial resolution , as boundary conditions are not only updated at every timestep, but the ...HIGH RESOLUTION MESOSCALE WEATHER DATA IMPROVEMENT TO SPATIAL EFFECTS FOR DOSE-RATE CONTOUR PLOT PREDICTIONS THESIS Christopher P...11 1 HIGH RESOLUTION MESOSCALE WEATHER DATA IMPROVEMENT TO SPATIAL EFFECTS FOR DOSE-RATE CONTOUR PLOT

Multimodal integration of fMRI and EEG data for high spatial and temporal resolution analysis of brain networks

PubMed Central

Mantini, D.; Marzetti, L.; Corbetta, M.; Romani, G.L.; Del Gratta, C.

2017-01-01

Two major non-invasive brain mapping techniques, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have complementary advantages with regard to their spatial and temporal resolution. We propose an approach based on the integration of EEG and fMRI, enabling the EEG temporal dynamics of information processing to be characterized within spatially well-defined fMRI large-scale networks. First, the fMRI data are decomposed into networks by means of spatial independent component analysis (sICA), and those associated with intrinsic activity and/or responding to task performance are selected using information from the related time-courses. Next, the EEG data over all sensors are averaged with respect to event timing, thus calculating event-related potentials (ERPs). The ERPs are subjected to temporal ICA (tICA), and the resulting components are localized with the weighted minimum norm (WMNLS) algorithm using the task-related fMRI networks as priors. Finally, the temporal contribution of each ERP component in the areas belonging to the fMRI large-scale networks is estimated. The proposed approach has been evaluated on visual target detection data. Our results confirm that two different components, commonly observed in EEG when presenting novel and salient stimuli respectively, are related to the neuronal activation in large-scale networks, operating at different latencies and associated with different functional processes. PMID:20052528

Efficient Data Mining for Local Binary Pattern in Texture Image Analysis

PubMed Central

Kwak, Jin Tae; Xu, Sheng; Wood, Bradford J.

2015-01-01

Local binary pattern (LBP) is a simple gray scale descriptor to characterize the local distribution of the grey levels in an image. Multi-resolution LBP and/or combinations of the LBPs have shown to be effective in texture image analysis. However, it is unclear what resolutions or combinations to choose for texture analysis. Examining all the possible cases is impractical and intractable due to the exponential growth in a feature space. This limits the accuracy and time- and space-efficiency of LBP. Here, we propose a data mining approach for LBP, which efficiently explores a high-dimensional feature space and finds a relatively smaller number of discriminative features. The features can be any combinations of LBPs. These may not be achievable with conventional approaches. Hence, our approach not only fully utilizes the capability of LBP but also maintains the low computational complexity. We incorporated three different descriptors (LBP, local contrast measure, and local directional derivative measure) with three spatial resolutions and evaluated our approach using two comprehensive texture databases. The results demonstrated the effectiveness and robustness of our approach to different experimental designs and texture images. PMID:25767332

Attention Modifies Spatial Resolution According to Task Demands.

PubMed

Barbot, Antoine; Carrasco, Marisa

2017-03-01

How does visual attention affect spatial resolution? In texture-segmentation tasks, exogenous (involuntary) attention automatically increases resolution at the attended location, which improves performance where resolution is too low (at the periphery) but impairs performance where resolution is already too high (at central locations). Conversely, endogenous (voluntary) attention improves performance at all eccentricities, which suggests a more flexible mechanism. Here, using selective adaptation to spatial frequency, we investigated the mechanism by which endogenous attention benefits performance in resolution tasks. Participants detected a texture target that could appear at several eccentricities. Adapting to high or low spatial frequencies selectively affected performance in a manner consistent with changes in resolution. Moreover, adapting to high, but not low, frequencies mitigated the attentional benefit at central locations where resolution was too high; this shows that attention can improve performance by decreasing resolution. Altogether, our results indicate that endogenous attention benefits performance by modulating the contribution of high-frequency information in order to flexibly adjust spatial resolution according to task demands.

Attention Modifies Spatial Resolution According to Task Demands

PubMed Central

Barbot, Antoine; Carrasco, Marisa

2017-01-01

How does visual attention affect spatial resolution? In texture-segmentation tasks, exogenous (involuntary) attention automatically increases resolution at the attended location, which improves performance where resolution is too low (at the periphery) but impairs performance where resolution is already too high (at central locations). Conversely, endogenous (voluntary) attention improves performance at all eccentricities, which suggests a more flexible mechanism. Here, using selective adaptation to spatial frequency, we investigated the mechanism by which endogenous attention benefits performance in resolution tasks. Participants detected a texture target that could appear at several eccentricities. Adapting to high or low spatial frequencies selectively affected performance in a manner consistent with changes in resolution. Moreover, adapting to high, but not low, frequencies mitigated the attentional benefit at central locations where resolution was too high; this shows that attention can improve performance by decreasing resolution. Altogether, our results indicate that endogenous attention benefits performance by modulating the contribution of high-frequency information in order to flexibly adjust spatial resolution according to task demands. PMID:28118103

AN ACTIVE-PASSIVE COMBINED ALGORITHM FOR HIGH SPATIAL RESOLUTION RETRIEVAL OF SOIL MOISTURE FROM SATELLITE SENSORS (Invited)

NASA Astrophysics Data System (ADS)

Lakshmi, V.; Mladenova, I. E.; Narayan, U.

2009-12-01

Soil moisture is known to be an essential factor in controlling the partitioning of rainfall into surface runoff and infiltration and solar energy into latent and sensible heat fluxes. Remote sensing has long proven its capability to obtain soil moisture in near real-time. However, at the present time we have the Advanced Scanning Microwave Radiometer (AMSR-E) on board NASA’s AQUA platform is the only satellite sensor that supplies a soil moisture product. AMSR-E coarse spatial resolution (~ 50 km at 6.9 GHz) strongly limits its applicability for small scale studies. A very promising technique for spatial disaggregation by combining radar and radiometer observations has been demonstrated by the authors using a methodology is based on the assumption that any change in measured brightness temperature and backscatter from one to the next time step is due primarily to change in soil wetness. The approach uses radiometric estimates of soil moisture at a lower resolution to compute the sensitivity of radar to soil moisture at the lower resolution. This estimate of sensitivity is then disaggregated using vegetation water content, vegetation type and soil texture information, which are the variables on which determine the radar sensitivity to soil moisture and are generally available at a scale of radar observation. This change detection algorithm is applied to several locations. We have used aircraft observed active and passive data over Walnut Creek watershed in Central Iowa in 2002; the Little Washita Watershed in Oklahoma in 2003 and the Murrumbidgee Catchment in southeastern Australia for 2006. All of these locations have different soils and land cover conditions which leads to a rigorous test of the disaggregation algorithm. Furthermore, we compare the derived high spatial resolution soil moisture to in-situ sampling and ground observation networks

SU-F-J-189: A Method to Improve the Spatial Resolution of Prompt Gamma Based Compton Imaging for Proton Range Verification

DOE Office of Scientific and Technical Information (OSTI.GOV)

Draeger, E; Chen, H; Polf, J

Purpose: To test two new techniques, the distance-of-closest approach (DCA) and Compton line (CL) filters, developed as a means of improving the spatial resolution of Compton camera (CC) imaging. Methods: Gammas emitted from {sup 22}Na, {sup 137}Cs, and {sup 60}Co point sources were measured with a prototype 3-stage CC. The energy deposited and position of each interaction in each stage were recorded and used to calculate a “cone-of-origin” for each gamma that scattered twice in the CC. A DCA filter was developed which finds the shortest distance from the gamma’s cone-of-origin surface to the location of the gamma source. Themore » DCA filter was applied to the data to determine the initial energy of the gamma and to remove “bad” interactions that only contribute noise to the image. Additionally, a CL filter, which removes gamma events that do not follow the theoretical predictions of the Compton scatter equation, was used to further remove “bad” interactions from the measured data. Then images were reconstructed with raw, unfiltered data, DCA filtered data, and DCA+CL filtered data and the achievable image resolution of each dataset was compared. Results: Spatial resolutions of ∼2 mm, and better than 2 mm, were achievable with the DCA and DCA+CL filtered data, respectively, compared to > 5 mm for the raw, unfiltered data. Conclusion: In many special cases in medical imaging where information about the source position may be known, such as proton radiotherapy range verification, the application of the DCA and CL filters can result in considerable improvements in the achievable spatial resolutions of Compton imaging.« less

Spatial resolution dependence on spectral frequency in human speech cortex electrocorticography

NASA Astrophysics Data System (ADS)

Muller, Leah; Hamilton, Liberty S.; Edwards, Erik; Bouchard, Kristofer E.; Chang, Edward F.

2016-10-01

Objective. Electrocorticography (ECoG) has become an important tool in human neuroscience and has tremendous potential for emerging applications in neural interface technology. Electrode array design parameters are outstanding issues for both research and clinical applications, and these parameters depend critically on the nature of the neural signals to be recorded. Here, we investigate the functional spatial resolution of neural signals recorded at the human cortical surface. We empirically derive spatial spread functions to quantify the shared neural activity for each frequency band of the electrocorticogram. Approach. Five subjects with high-density (4 mm center-to-center spacing) ECoG grid implants participated in speech perception and production tasks while neural activity was recorded from the speech cortex, including superior temporal gyrus, precentral gyrus, and postcentral gyrus. The cortical surface field potential was decomposed into traditional EEG frequency bands. Signal similarity between electrode pairs for each frequency band was quantified using a Pearson correlation coefficient. Main results. The correlation of neural activity between electrode pairs was inversely related to the distance between the electrodes; this relationship was used to quantify spatial falloff functions for cortical subdomains. As expected, lower frequencies remained correlated over larger distances than higher frequencies. However, both the envelope and phase of gamma and high gamma frequencies (30-150 Hz) are largely uncorrelated (<90%) at 4 mm, the smallest spacing of the high-density arrays. Thus, ECoG arrays smaller than 4 mm have significant promise for increasing signal resolution at high frequencies, whereas less additional gain is achieved for lower frequencies. Significance. Our findings quantitatively demonstrate the dependence of ECoG spatial resolution on the neural frequency of interest. We demonstrate that this relationship is consistent across patients and across cortical areas during activity.

Simulation of semi-arid hydrological processes at different spatial resolutions using the AgroEcoSystem-Watershed (AgES-W) model

NASA Astrophysics Data System (ADS)

Green, T. R.; Erksine, R. H.; David, O.; Ascough, J. C., II; Kipka, H.; Lloyd, W. J.; McMaster, G. S.

2015-12-01

Water movement and storage within a watershed may be simulated at different spatial resolutions of land areas or hydrological response units (HRUs). Here, effects of HRU size on simulated soil water and surface runoff are tested using the AgroEcoSystem-Watershed (AgES-W) model with three different resolutions of HRUs. We studied a 56-ha agricultural watershed in northern Colorado, USA farmed primarily under a wheat-fallow rotation. The delineation algorithm was based upon topography (surface flow paths), land use (crop management strips and native grass), and mapped soil units (three types), which produced HRUs that follow the land use and soil boundaries. AgES-W model parameters that control surface and subsurface hydrology were calibrated using simulated daily soil moisture at different landscape positions and depths where soil moisture was measured hourly and averaged up to daily values. Parameter sets were both uniform and spatially variable with depth and across the watershed (5 different calibration approaches). Although forward simulations were computationally efficient (less than 1 minute each), each calibration required thousands of model runs. Execution of such large jobs was facilitated by using the Object Modeling System with the Cloud Services Innovation Platform to manage four virtual machines on a commercial web service configured with a total of 64 computational cores and 120 GB of memory. Results show how spatially distributed and averaged soil moisture and runoff at the outlet vary with different HRU delineations. The results will help guide HRU delineation, spatial resolution and parameter estimation methods for improved hydrological simulations in this and other semi-arid agricultural watersheds.

Effects of spatial resolution

NASA Technical Reports Server (NTRS)

Abrams, M.

1982-01-01

Studies of the effects of spatial resolution on extraction of geologic information are woefully lacking but spatial resolution effects can be examined as they influence two general categories: detection of spatial features per se; and the effects of IFOV on the definition of spectral signatures and on general mapping abilities.

Stellar interferometers and hypertelescopes: new insights on an angular spatial frequency approach to their non-invariant imaging

NASA Astrophysics Data System (ADS)

Dettwiller, L.; Lépine, T.

2017-12-01

A general and pure wave theory of image formation for all types of stellar interferometers, including hypertelescopes, is developed in the frame of Fresnel's paraxial approximations of diffraction. For a hypertelescope, we show that the severe lack of translation invariance leads to multiple and strong spatial frequency heterodyning, which codes the very high frequencies detected by the hypertelescope into medium spatial frequencies and introduces a moiré-type ambiguity for extended objects. This explains mathematically the disappointing appearance of poor resolution observed in some image simulations for hypertelescopes.

Spatial, Temporal and Spectral Satellite Image Fusion via Sparse Representation

NASA Astrophysics Data System (ADS)

Song, Huihui

Remote sensing provides good measurements for monitoring and further analyzing the climate change, dynamics of ecosystem, and human activities in global or regional scales. Over the past two decades, the number of launched satellite sensors has been increasing with the development of aerospace technologies and the growing requirements on remote sensing data in a vast amount of application fields. However, a key technological challenge confronting these sensors is that they tradeoff between spatial resolution and other properties, including temporal resolution, spectral resolution, swath width, etc., due to the limitations of hardware technology and budget constraints. To increase the spatial resolution of data with other good properties, one possible cost-effective solution is to explore data integration methods that can fuse multi-resolution data from multiple sensors, thereby enhancing the application capabilities of available remote sensing data. In this thesis, we propose to fuse the spatial resolution with temporal resolution and spectral resolution, respectively, based on sparse representation theory. Taking the study case of Landsat ETM+ (with spatial resolution of 30m and temporal resolution of 16 days) and MODIS (with spatial resolution of 250m ~ 1km and daily temporal resolution) reflectance, we propose two spatial-temporal fusion methods to combine the fine spatial information of Landsat image and the daily temporal resolution of MODIS image. Motivated by that the images from these two sensors are comparable on corresponding bands, we propose to link their spatial information on available Landsat- MODIS image pair (captured on prior date) and then predict the Landsat image from the MODIS counterpart on prediction date. To well-learn the spatial details from the prior images, we use a redundant dictionary to extract the basic representation atoms for both Landsat and MODIS images based on sparse representation. Under the scenario of two prior Landsat-MODIS image pairs, we build the corresponding relationship between the difference images of MODIS and ETM+ by training a low- and high-resolution dictionary pair from the given prior image pairs. In the second scenario, i.e., only one Landsat- MODIS image pair being available, we directly correlate MODIS and ETM+ data through an image degradation model. Then, the fusion stage is achieved by super-resolving the MODIS image combining the high-pass modulation in a two-layer fusion framework. Remarkably, the proposed spatial-temporal fusion methods form a unified framework for blending remote sensing images with phenology change or land-cover-type change. Based on the proposed spatial-temporal fusion models, we propose to monitor the land use/land cover changes in Shenzhen, China. As a fast-growing city, Shenzhen faces the problem of detecting the rapid changes for both rational city planning and sustainable development. However, the cloudy and rainy weather in region Shenzhen located makes the capturing circle of high-quality satellite images longer than their normal revisit periods. Spatial-temporal fusion methods are capable to tackle this problem by improving the spatial resolution of images with coarse spatial resolution but frequent temporal coverage, thereby making the detection of rapid changes possible. On two Landsat-MODIS datasets with annual and monthly changes, respectively, we apply the proposed spatial-temporal fusion methods to the task of multiple change detection. Afterward, we propose a novel spatial and spectral fusion method for satellite multispectral and hyperspectral (or high-spectral) images based on dictionary-pair learning and sparse non-negative matrix factorization. By combining the spectral information from hyperspectral image, which is characterized by low spatial resolution but high spectral resolution and abbreviated as LSHS, and the spatial information from multispectral image, which is featured by high spatial resolution but low spectral resolution and abbreviated as HSLS, this method aims to generate the fused data with both high spatial and high spectral resolutions. Motivated by the observation that each hyperspectral pixel can be represented by a linear combination of a few endmembers, this method first extracts the spectral bases of LSHS and HSLS images by making full use of the rich spectral information in LSHS data. The spectral bases of these two categories data then formulate a dictionary-pair due to their correspondence in representing each pixel spectra of LSHS data and HSLS data, respectively. Subsequently, the LSHS image is spatially unmixed by representing the HSLS image with respect to the corresponding learned dictionary to derive its representation coefficients. Combining the spectral bases of LSHS data and the representation coefficients of HSLS data, we finally derive the fused data characterized by the spectral resolution of LSHS data and the spatial resolution of HSLS data.

Estimation of net ecosystem production in Asia using the diagnostic-type ecosystem model with a 10 km grid-scale resolution

NASA Astrophysics Data System (ADS)

Sasai, Takahiro; Obikawa, Hiroki; Murakami, Kazutaka; Kato, Soushi; Matsunaga, Tsuneo; Nemani, Ramakrishna R.

2016-06-01

The terrestrial carbon cycle in Asia is highly uncertain, and it affects our understanding of global warming. One of the important issues is the need for an enhancement of spatial resolution, since local regions in Asia are heterogeneous with regard to meteorology, land form, and land cover type, which greatly impacts the detailed spatial patterns in its ecosystem. Thus, an important goal of this study is to reasonably reproduce the heterogeneous biogeochemical patterns in Asia by enhancing the spatial resolution of the ecosystem model biosphere model integrating eco-physiological and mechanistic approaches using satellite data (BEAMS). We estimated net ecosystem production (NEP) over eastern Asia and examined the spatial differences in the factors controlling NEP by using a 10 km grid-scale approach over two different decades (2001-2010 and 2091-2100). The present and future meteorological inputs were derived from satellite observations and the downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) data set, respectively. The results showed that the present NEP in whole eastern Asia was carbon source (-214.9 TgC yr-1) and in future scenarios, the greatest positive (76.4 TgC yr-1) and least negative (-95.9 TgC yr-1) NEPs were estimated from the Representative Concentration Pathways (RCP) 6.0 and RCP8.5 scenarios, respectively. Calculated annual NEP in RCP8.5 was mostly positive in the southern part of East Asia and Southeast Asia and negative in northern and central parts of East Asia. Under the RCP scenario with higher greenhouse gases emission (RCP8.5), deciduous needleleaf and mixed forests distributed in the middle and high latitudes served as carbon source. In contrast, evergreen broadleaf forests distributed in low latitudes served as carbon sink. The sensitivity study demonstrated that the spatial tendency of NEP was largely influenced by atmospheric CO2 and temperature.

The Australian National Airborne Field Experiment 2005: Soil Moisture Remote Sensing at 60 Meter Resolution and Up

NASA Technical Reports Server (NTRS)

Kim, E. J.; Walker, J. P.; Panciera, R.; Kalma, J. D.

2006-01-01

Spatially-distributed soil moisture observations have applications spanning a wide range of spatial resolutions from the very local needs of individual farmers to the progressively larger areas of interest to weather forecasters, water resource managers, and global climate modelers. To date, the most promising approach for space-based remote sensing of soil moisture makes use of passive microwave emission radiometers at L-band frequencies (1-2 GHz). Several soil moisture-sensing satellites have been proposed in recent years, with the European Space Agency's Soil Moisture Ocean Salinity (SMOS) mission scheduled to be launched first in a couple years. While such a microwave-based approach has the advantage of essentially allweather operation, satellite size limits spatial resolution to 10's of km. Whether used at this native resolution or in conjunction with some type of downscaling technique to generate soil moisture estimates on a finer-scale grid, the effects of subpixel spatial variability play a critical role. The soil moisture variability is typically affected by factors such as vegetation, topography, surface roughness, and soil texture. Understanding and these factors is the key to achieving accurate soil moisture retrievals at any scale. Indeed, the ability to compensate for these factors ultimately limits the achievable spatial resolution and/or accuracy of the retrieval. Over the last 20 years, a series of airborne campaigns in the USA have supported the development of algorithms for spaceborne soil moisture retrieval. The most important observations involved imagery from passive microwave radiometers. The early campaigns proved that the retrieval worked for larger and larger footprints, up to satellite-scale footprints. These provided the solid basis for proposing the satellite missions. More recent campaigns have explored other aspects such as retrieval performance through greater amounts of vegetation. All of these campaigns featured extensive ground truth collection over a range of grid spacings, to provide a basis for examining the effects of subpixel variability. However, the native footprint size of the airborne L-band radiometers was always a few hundred meters. During the recently completed (November, 2005) National Airborne Field Experiment (NAFE) campaign in Australia, a compact L-band radiometer was deployed on a small aircraft. This new combination permitted routine observations at native resolutions as high as 60 meters, substantially finer than in previous airborne soil moisture campaigns, as well as satellite footprint areal coverage. The radiometer, the Polarimetric L-band Microwave Radiometer (PLMR) performed extremely well and operations included extensive calibration-related observations. Thus, along with the extensive fine-scale ground truth, the NAFE dataset includes all the ingredients for the first scaling studies involving very-high-native resolution soil moisture observations and the effects of vegetation, roughness, etc. A brief overview of the NAFE will be presented, then examples of the airborne observations with resolutions from 60 m to 1 km will be shown, and early results from scaling studies will be discussed.

Long-Term Quantitative Precipitation Estimates (QPE) at High Spatial and Temporal Resolution over CONUS: Bias-Adjustment of the Radar-Only National Mosaic and Multi-sensor QPE (NMQ/Q2) Precipitation Reanalysis (2001-2012)

NASA Astrophysics Data System (ADS)

Prat, Olivier; Nelson, Brian; Stevens, Scott; Seo, Dong-Jun; Kim, Beomgeun

2015-04-01

The processing of radar-only precipitation via the reanalysis from the National Mosaic and Multi-Sensor Quantitative (NMQ/Q2) based on the WSR-88D Next-generation Radar (NEXRAD) network over Continental United States (CONUS) is completed for the period covering from 2001 to 2012. This important milestone constitutes a unique opportunity to study precipitation processes at a 1-km spatial resolution for a 5-min temporal resolution. However, in order to be suitable for hydrological, meteorological and climatological applications, the radar-only product needs to be bias-adjusted and merged with in-situ rain gauge information. Several in-situ datasets are available to assess the biases of the radar-only product and to adjust for those biases to provide a multi-sensor QPE. The rain gauge networks that are used such as the Global Historical Climatology Network-Daily (GHCN-D), the Hydrometeorological Automated Data System (HADS), the Automated Surface Observing Systems (ASOS), and the Climate Reference Network (CRN), have different spatial density and temporal resolution. The challenges related to incorporating non-homogeneous networks over a vast area and for a long-term record are enormous. Among the challenges we are facing are the difficulties incorporating differing resolution and quality surface measurements to adjust gridded estimates of precipitation. Another challenge is the type of adjustment technique. The objective of this work is threefold. First, we investigate how the different in-situ networks can impact the precipitation estimates as a function of the spatial density, sensor type, and temporal resolution. Second, we assess conditional and un-conditional biases of the radar-only QPE for various time scales (daily, hourly, 5-min) using in-situ precipitation observations. Finally, after assessing the bias and applying reduction or elimination techniques, we are using a unique in-situ dataset merging the different RG networks (CRN, ASOS, HADS, GHCN-D) to adjust the radar-only QPE product via an Inverse Distance Weighting (IDW) approach. In addition, we also investigate alternate adjustment techniques such as the kriging method and its variants (Simple Kriging: SK; Ordinary Kriging: OK; Conditional Bias-Penalized Kriging: CBPK). From this approach, we also hope to generate estimates of uncertainty for the gridded bias-adjusted QPE. Further comparison with a suite of lower resolution QPEs derived from ground based radar measurements (Stage IV) and satellite products (TMPA, CMORPH, PERSIANN) is also provided in order to give a detailed picture of the improvements and remaining challenges.

Distributed fiber-optic temperature sensing for hydrologic systems

NASA Astrophysics Data System (ADS)

Selker, John S.; ThéVenaz, Luc; Huwald, Hendrik; Mallet, Alfred; Luxemburg, Wim; van de Giesen, Nick; Stejskal, Martin; Zeman, Josef; Westhoff, Martijn; Parlange, Marc B.

2006-12-01

Instruments for distributed fiber-optic measurement of temperature are now available with temperature resolution of 0.01°C and spatial resolution of 1 m with temporal resolution of fractions of a minute along standard fiber-optic cables used for communication with lengths of up to 30,000 m. We discuss the spectrum of fiber-optic tools that may be employed to make these measurements, illuminating the potential and limitations of these methods in hydrologic science. There are trade-offs between precision in temperature, temporal resolution, and spatial resolution, following the square root of the number of measurements made; thus brief, short measurements are less precise than measurements taken over longer spans in time and space. Five illustrative applications demonstrate configurations where the distributed temperature sensing (DTS) approach could be used: (1) lake bottom temperatures using existing communication cables, (2) temperature profile with depth in a 1400 m deep decommissioned mine shaft, (3) air-snow interface temperature profile above a snow-covered glacier, (4) air-water interfacial temperature in a lake, and (5) temperature distribution along a first-order stream. In examples 3 and 4 it is shown that by winding the fiber around a cylinder, vertical spatial resolution of millimeters can be achieved. These tools may be of exceptional utility in observing a broad range of hydrologic processes, including evaporation, infiltration, limnology, and the local and overall energy budget spanning scales from 0.003 to 30,000 m. This range of scales corresponds well with many of the areas of greatest opportunity for discovery in hydrologic science.

Spatially Resolving Ocean Color and Sediment Dispersion in River Plumes, Coastal Systems, and Continental Shelf Waters

NASA Technical Reports Server (NTRS)

Aurin, Dirk Alexander; Mannino, Antonio; Franz, Bryan

2013-01-01

Satellite remote sensing of ocean color in dynamic coastal, inland, and nearshorewaters is impeded by high variability in optical constituents, demands specialized atmospheric correction, and is limited by instrument sensitivity. To accurately detect dispersion of bio-optical properties, remote sensors require ample signal-to-noise ratio (SNR) to sense small variations in ocean color without saturating over bright pixels, an atmospheric correction that can accommodate significantwater-leaving radiance in the near infrared (NIR), and spatial and temporal resolution that coincides with the scales of variability in the environment. Several current and historic space-borne sensors have met these requirements with success in the open ocean, but are not optimized for highly red-reflective and heterogeneous waters such as those found near river outflows or in the presence of sediment resuspension. Here we apply analytical approaches for determining optimal spatial resolution, dominant spatial scales of variability ("patches"), and proportions of patch variability that can be resolved from four river plumes around the world between 2008 and 2011. An offshore region in the Sargasso Sea is analyzed for comparison. A method is presented for processing Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra imagery including cloud detection, stray lightmasking, faulty detector avoidance, and dynamic aerosol correction using short-wave- and near-infrared wavebands in extremely turbid regions which pose distinct optical and technical challenges. Results showthat a pixel size of approx. 520 mor smaller is generally required to resolve spatial heterogeneity in ocean color and total suspended materials in river plumes. Optimal pixel size increases with distance from shore to approx. 630 m in nearshore regions, approx 750 m on the continental shelf, and approx. 1350 m in the open ocean. Greater than 90% of the optical variability within plume regions is resolvable with 500 m resolution, and small, but significant, differences were found between peak and nadir river flow periods in terms of optimal resolution and resolvable proportion of variability.

Results of the spatial resolution simulation for multispectral data (resolution brochures)

NASA Technical Reports Server (NTRS)

1982-01-01

The variable information content of Earth Resource products at different levels of spatial resolution and in different spectral bands is addressed. A low-cost brochure that scientists and laymen could use to visualize the effects of increasing the spatial resolution of multispectral scanner images was produced.

A priori and a posteriori analysis of the flow around a rectangular cylinder

NASA Astrophysics Data System (ADS)

Cimarelli, A.; Leonforte, A.; Franciolini, M.; De Angelis, E.; Angeli, D.; Crivellini, A.

2017-11-01

The definition of a correct mesh resolution and modelling approach for the Large Eddy Simulation (LES) of the flow around a rectangular cylinder is recognized to be a rather elusive problem as shown by the large scatter of LES results present in the literature. In the present work, we aim at assessing this issue by performing an a priori analysis of Direct Numerical Simulation (DNS) data of the flow. This approach allows us to measure the ability of the LES field on reproducing the main flow features as a function of the resolution employed. Based on these results, we define a mesh resolution which maximize the opposite needs of reducing the computational costs and of adequately resolving the flow dynamics. The effectiveness of the resolution method proposed is then verified by means of an a posteriori analysis of actual LES data obtained by means of the implicit LES approach given by the numerical properties of the Discontinuous Galerkin spatial discretization technique. The present work represents a first step towards a best practice for LES of separating and reattaching flows.

Single-pulse enhanced coherent diffraction imaging of bacteria with an X-ray free-electron laser

NASA Astrophysics Data System (ADS)

Fan, Jiadong; Sun, Zhibin; Wang, Yaling; Park, Jaehyun; Kim, Sunam; Gallagher-Jones, Marcus; Kim, Yoonhee; Song, Changyong; Yao, Shengkun; Zhang, Jian; Zhang, Jianhua; Duan, Xiulan; Tono, Kensuke; Yabashi, Makina; Ishikawa, Tetsuya; Fan, Chunhai; Zhao, Yuliang; Chai, Zhifang; Gao, Xueyun; Earnest, Thomas; Jiang, Huaidong

2016-09-01

High-resolution imaging offers one of the most promising approaches for exploring and understanding the structure and function of biomaterials and biological systems. X-ray free-electron lasers (XFELs) combined with coherent diffraction imaging can theoretically provide high-resolution spatial information regarding biological materials using a single XFEL pulse. Currently, the application of this method suffers from the low scattering cross-section of biomaterials and X-ray damage to the sample. However, XFELs can provide pulses of such short duration that the data can be collected using the “diffract and destroy” approach before the effects of radiation damage on the data become significant. These experiments combine the use of enhanced coherent diffraction imaging with single-shot XFEL radiation to investigate the cellular architecture of Staphylococcus aureus with and without labeling by gold (Au) nanoclusters. The resolution of the images reconstructed from these diffraction patterns were twice as high or more for gold-labeled samples, demonstrating that this enhancement method provides a promising approach for the high-resolution imaging of biomaterials and biological systems.

Single-pulse enhanced coherent diffraction imaging of bacteria with an X-ray free-electron laser

PubMed Central

Fan, Jiadong; Sun, Zhibin; Wang, Yaling; Park, Jaehyun; Kim, Sunam; Gallagher-Jones, Marcus; Kim, Yoonhee; Song, Changyong; Yao, Shengkun; Zhang, Jian; Zhang, Jianhua; Duan, Xiulan; Tono, Kensuke; Yabashi, Makina; Ishikawa, Tetsuya; Fan, Chunhai; Zhao, Yuliang; Chai, Zhifang; Gao, Xueyun; Earnest, Thomas; Jiang, Huaidong

2016-01-01

High-resolution imaging offers one of the most promising approaches for exploring and understanding the structure and function of biomaterials and biological systems. X-ray free-electron lasers (XFELs) combined with coherent diffraction imaging can theoretically provide high-resolution spatial information regarding biological materials using a single XFEL pulse. Currently, the application of this method suffers from the low scattering cross-section of biomaterials and X-ray damage to the sample. However, XFELs can provide pulses of such short duration that the data can be collected using the “diffract and destroy” approach before the effects of radiation damage on the data become significant. These experiments combine the use of enhanced coherent diffraction imaging with single-shot XFEL radiation to investigate the cellular architecture of Staphylococcus aureus with and without labeling by gold (Au) nanoclusters. The resolution of the images reconstructed from these diffraction patterns were twice as high or more for gold-labeled samples, demonstrating that this enhancement method provides a promising approach for the high-resolution imaging of biomaterials and biological systems. PMID:27659203

Single-pulse enhanced coherent diffraction imaging of bacteria with an X-ray free-electron laser.

PubMed

Fan, Jiadong; Sun, Zhibin; Wang, Yaling; Park, Jaehyun; Kim, Sunam; Gallagher-Jones, Marcus; Kim, Yoonhee; Song, Changyong; Yao, Shengkun; Zhang, Jian; Zhang, Jianhua; Duan, Xiulan; Tono, Kensuke; Yabashi, Makina; Ishikawa, Tetsuya; Fan, Chunhai; Zhao, Yuliang; Chai, Zhifang; Gao, Xueyun; Earnest, Thomas; Jiang, Huaidong

2016-09-23

High-resolution imaging offers one of the most promising approaches for exploring and understanding the structure and function of biomaterials and biological systems. X-ray free-electron lasers (XFELs) combined with coherent diffraction imaging can theoretically provide high-resolution spatial information regarding biological materials using a single XFEL pulse. Currently, the application of this method suffers from the low scattering cross-section of biomaterials and X-ray damage to the sample. However, XFELs can provide pulses of such short duration that the data can be collected using the "diffract and destroy" approach before the effects of radiation damage on the data become significant. These experiments combine the use of enhanced coherent diffraction imaging with single-shot XFEL radiation to investigate the cellular architecture of Staphylococcus aureus with and without labeling by gold (Au) nanoclusters. The resolution of the images reconstructed from these diffraction patterns were twice as high or more for gold-labeled samples, demonstrating that this enhancement method provides a promising approach for the high-resolution imaging of biomaterials and biological systems.

CCTV Coverage Index Based on Surveillance Resolution and Its Evaluation Using 3D Spatial Analysis

PubMed Central

Choi, Kyoungah; Lee, Impyeong

2015-01-01

We propose a novel approach to evaluating how effectively a closed circuit television (CCTV) system can monitor a targeted area. With 3D models of the target area and the camera parameters of the CCTV system, the approach produces surveillance coverage index, which is newly defined in this study as a quantitative measure for surveillance performance. This index indicates the proportion of the space being monitored with a sufficient resolution to the entire space of the target area. It is determined by computing surveillance resolution at every position and orientation, which indicates how closely a specific object can be monitored with a CCTV system. We present full mathematical derivation for the resolution, which depends on the location and orientation of the object as well as the geometric model of a camera. With the proposed approach, we quantitatively evaluated the surveillance coverage of a CCTV system in an underground parking area. Our evaluation process provided various quantitative-analysis results, compelling us to examine the design of the CCTV system prior to its installation and understand the surveillance capability of an existing CCTV system. PMID:26389909

Assessment of a vertical high-resolution distributed-temperature-sensing system in a shallow thermohaline environment

NASA Astrophysics Data System (ADS)

Suárez, F.; Aravena, J. E.; Hausner, M. B.; Childress, A. E.; Tyler, S. W.

2011-01-01

In shallow thermohaline-driven lakes it is important to measure temperature on fine spatial and temporal scales to detect stratification or different hydrodynamic regimes. Raman spectra distributed temperature sensing (DTS) is an approach available to provide high spatial and temporal temperature resolution. A vertical high-resolution DTS system was constructed to overcome the problems of typical methods used in the past, i.e., without disturbing the water column, and with resistance to corrosive environments. This system monitors the temperature profile each 1.1 cm vertically and in time averages as small as 10 s. Temperature resolution as low as 0.035 °C is obtained when the data are collected at 5-min intervals. The vertical high-resolution DTS system is used to monitor the thermal behavior of a salt-gradient solar pond, which is an engineered shallow thermohaline system that allows collection and storage of solar energy for a long period of time. This paper describes a method to quantitatively assess accuracy, precision and other limitations of DTS systems to fully utilize the capacity of this technology. It also presents, for the first time, a method to manually calibrate temperatures along the optical fiber.

Sparse Bayesian Inference of White Matter Fiber Orientations from Compressed Multi-resolution Diffusion MRI

PubMed Central

Pisharady, Pramod Kumar; Duarte-Carvajalino, Julio M; Sotiropoulos, Stamatios N; Sapiro, Guillermo; Lenglet, Christophe

2017-01-01

The RubiX [1] algorithm combines high SNR characteristics of low resolution data with high spacial specificity of high resolution data, to extract microstructural tissue parameters from diffusion MRI. In this paper we focus on estimating crossing fiber orientations and introduce sparsity to the RubiX algorithm, making it suitable for reconstruction from compressed (under-sampled) data. We propose a sparse Bayesian algorithm for estimation of fiber orientations and volume fractions from compressed diffusion MRI. The data at high resolution is modeled using a parametric spherical deconvolution approach and represented using a dictionary created with the exponential decay components along different possible directions. Volume fractions of fibers along these orientations define the dictionary weights. The data at low resolution is modeled using a spatial partial volume representation. The proposed dictionary representation and sparsity priors consider the dependence between fiber orientations and the spatial redundancy in data representation. Our method exploits the sparsity of fiber orientations, therefore facilitating inference from under-sampled data. Experimental results show improved accuracy and decreased uncertainty in fiber orientation estimates. For under-sampled data, the proposed method is also shown to produce more robust estimates of fiber orientations. PMID:28845484

Sparse Bayesian Inference of White Matter Fiber Orientations from Compressed Multi-resolution Diffusion MRI.

PubMed

Pisharady, Pramod Kumar; Duarte-Carvajalino, Julio M; Sotiropoulos, Stamatios N; Sapiro, Guillermo; Lenglet, Christophe

2015-10-01

The RubiX [1] algorithm combines high SNR characteristics of low resolution data with high spacial specificity of high resolution data, to extract microstructural tissue parameters from diffusion MRI. In this paper we focus on estimating crossing fiber orientations and introduce sparsity to the RubiX algorithm, making it suitable for reconstruction from compressed (under-sampled) data. We propose a sparse Bayesian algorithm for estimation of fiber orientations and volume fractions from compressed diffusion MRI. The data at high resolution is modeled using a parametric spherical deconvolution approach and represented using a dictionary created with the exponential decay components along different possible directions. Volume fractions of fibers along these orientations define the dictionary weights. The data at low resolution is modeled using a spatial partial volume representation. The proposed dictionary representation and sparsity priors consider the dependence between fiber orientations and the spatial redundancy in data representation. Our method exploits the sparsity of fiber orientations, therefore facilitating inference from under-sampled data. Experimental results show improved accuracy and decreased uncertainty in fiber orientation estimates. For under-sampled data, the proposed method is also shown to produce more robust estimates of fiber orientations.

Image sharpening for mixed spatial and spectral resolution satellite systems

NASA Technical Reports Server (NTRS)

Hallada, W. A.; Cox, S.

1983-01-01

Two methods of image sharpening (reconstruction) are compared. The first, a spatial filtering technique, extrapolates edge information from a high spatial resolution panchromatic band at 10 meters and adds it to the low spatial resolution narrow spectral bands. The second method, a color normalizing technique, is based on the ability to separate image hue and brightness components in spectral data. Using both techniques, multispectral images are sharpened from 30, 50, 70, and 90 meter resolutions. Error rates are calculated for the two methods and all sharpened resolutions. The results indicate that the color normalizing method is superior to the spatial filtering technique.

Spatial Resolution Requirements for Accurate Identification of Drivers of Atrial Fibrillation

PubMed Central

Roney, Caroline H.; Cantwell, Chris D.; Bayer, Jason D.; Qureshi, Norman A.; Lim, Phang Boon; Tweedy, Jennifer H.; Kanagaratnam, Prapa; Vigmond, Edward J.; Ng, Fu Siong

2017-01-01

Background— Recent studies have demonstrated conflicting mechanisms underlying atrial fibrillation (AF), with the spatial resolution of data often cited as a potential reason for the disagreement. The purpose of this study was to investigate whether the variation in spatial resolution of mapping may lead to misinterpretation of the underlying mechanism in persistent AF. Methods and Results— Simulations of rotors and focal sources were performed to estimate the minimum number of recording points required to correctly identify the underlying AF mechanism. The effects of different data types (action potentials and unipolar or bipolar electrograms) and rotor stability on resolution requirements were investigated. We also determined the ability of clinically used endocardial catheters to identify AF mechanisms using clinically recorded and simulated data. The spatial resolution required for correct identification of rotors and focal sources is a linear function of spatial wavelength (the distance between wavefronts) of the arrhythmia. Rotor localization errors are larger for electrogram data than for action potential data. Stationary rotors are more reliably identified compared with meandering trajectories, for any given spatial resolution. All clinical high-resolution multipolar catheters are of sufficient resolution to accurately detect and track rotors when placed over the rotor core although the low-resolution basket catheter is prone to false detections and may incorrectly identify rotors that are not present. Conclusions— The spatial resolution of AF data can significantly affect the interpretation of the underlying AF mechanism. Therefore, the interpretation of human AF data must be taken in the context of the spatial resolution of the recordings. PMID:28500175

Future lab-on-a-chip technologies for interrogating individual molecules.

PubMed

Craighead, Harold

2006-07-27

Advances in technology have allowed chemical sampling with high spatial resolution and the manipulation and measurement of individual molecules. Adaptation of these approaches to lab-on-a-chip formats is providing a new class of research tools for the investigation of biochemistry and life processes.

Compressed Sensing for Resolution Enhancement of Hyperpolarized 13C Flyback 3D-MRSI

PubMed Central

Hu, Simon; Lustig, Michael; Chen, Albert P.; Crane, Jason; Kerr, Adam; Kelley, Douglas A.C.; Hurd, Ralph; Kurhanewicz, John; Nelson, Sarah J.; Pauly, John M.; Vigneron, Daniel B.

2008-01-01

High polarization of nuclear spins in liquid state through dynamic nuclear polarization has enabled the direct monitoring of 13C metabolites in vivo at very high signal to noise, allowing for rapid assessment of tissue metabolism. The abundant SNR afforded by this hyperpolarization technique makes high resolution 13C 3D-MRSI feasible. However, the number of phase encodes that can be fit into the short acquisition time for hyperpolarized imaging limits spatial coverage and resolution. To take advantage of the high SNR available from hyperpolarization, we have applied compressed sensing to achieve a factor of 2 enhancement in spatial resolution without increasing acquisition time or decreasing coverage. In this paper, the design and testing of compressed sensing suited for a flyback 13C 3D-MRSI sequence are presented. The key to this design was the undersampling of spectral k-space using a novel blipped scheme, thus taking advantage of the considerable sparsity in typical hyperpolarized 13C spectra. Phantom tests validated the accuracy of the compressed sensing approach and initial mouse experiments demonstrated in vivo feasibility. PMID:18367420

Super-resolution reconstruction of MR image with a novel residual learning network algorithm

NASA Astrophysics Data System (ADS)

Shi, Jun; Liu, Qingping; Wang, Chaofeng; Zhang, Qi; Ying, Shihui; Xu, Haoyu

2018-04-01

Spatial resolution is one of the key parameters of magnetic resonance imaging (MRI). The image super-resolution (SR) technique offers an alternative approach to improve the spatial resolution of MRI due to its simplicity. Convolutional neural networks (CNN)-based SR algorithms have achieved state-of-the-art performance, in which the global residual learning (GRL) strategy is now commonly used due to its effectiveness for learning image details for SR. However, the partial loss of image details usually happens in a very deep network due to the degradation problem. In this work, we propose a novel residual learning-based SR algorithm for MRI, which combines both multi-scale GRL and shallow network block-based local residual learning (LRL). The proposed LRL module works effectively in capturing high-frequency details by learning local residuals. One simulated MRI dataset and two real MRI datasets have been used to evaluate our algorithm. The experimental results show that the proposed SR algorithm achieves superior performance to all of the other compared CNN-based SR algorithms in this work.

Spatial downscaling algorithm of TRMM precipitation based on multiple high-resolution satellite data for Inner Mongolia, China

NASA Astrophysics Data System (ADS)

Duan, Limin; Fan, Keke; Li, Wei; Liu, Tingxi

2017-12-01

Daily precipitation data from 42 stations in Inner Mongolia, China for the 10 years period from 1 January 2001 to 31 December 2010 was utilized along with downscaled data from the Tropical Rainfall Measuring Mission (TRMM) with a spatial resolution of 0.25° × 0.25° for the same period based on the statistical relationships between the normalized difference vegetation index (NDVI), meteorological variables, and digital elevation models (https://en.wikipedia.org/wiki/Digital_elevation_model) (DEM) using the leave-one-out (LOO) cross validation method and multivariate step regression. The results indicate that (1) TRMM data can indeed be used to estimate annual precipitation in Inner Mongolia and there is a linear relationship between annual TRMM and observed precipitation; (2) there is a significant relationship between TRMM-based precipitation and predicted precipitation, with a spatial resolution of 0.50° × 0.50°; (3) NDVI and temperature are important factors influencing the downscaling of TRMM precipitation data for DEM and the slope is not the most significant factor affecting the downscaled TRMM data; and (4) the downscaled TRMM data reflects spatial patterns in annual precipitation reasonably well, showing less precipitation falling in west Inner Mongolia and more in the south and southeast. The new approach proposed here provides a useful alternative for evaluating spatial patterns in precipitation and can thus be applied to generate a more accurate precipitation dataset to support both irrigation management and the conservation of this fragile grassland ecosystem.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kirtley, John R., E-mail: jkirtley@stanford.edu; Rosenberg, Aaron J.; Palmstrom, Johanna C.

Superconducting QUantum Interference Device (SQUID) microscopy has excellent magnetic field sensitivity, but suffers from modest spatial resolution when compared with other scanning probes. This spatial resolution is determined by both the size of the field sensitive area and the spacing between this area and the sample surface. In this paper we describe scanning SQUID susceptometers that achieve sub-micron spatial resolution while retaining a white noise floor flux sensitivity of ≈2μΦ{sub 0}/Hz{sup 1/2}. This high spatial resolution is accomplished by deep sub-micron feature sizes, well shielded pickup loops fabricated using a planarized process, and a deep etch step that minimizes themore » spacing between the sample surface and the SQUID pickup loop. We describe the design, modeling, fabrication, and testing of these sensors. Although sub-micron spatial resolution has been achieved previously in scanning SQUID sensors, our sensors not only achieve high spatial resolution but also have integrated modulation coils for flux feedback, integrated field coils for susceptibility measurements, and batch processing. They are therefore a generally applicable tool for imaging sample magnetization, currents, and susceptibilities with higher spatial resolution than previous susceptometers.« less

A weighted optimization approach to time-of-flight sensor fusion.

PubMed

Schwarz, Sebastian; Sjostrom, Marten; Olsson, Roger

2014-01-01

Acquiring scenery depth is a fundamental task in computer vision, with many applications in manufacturing, surveillance, or robotics relying on accurate scenery information. Time-of-flight cameras can provide depth information in real-time and overcome short-comings of traditional stereo analysis. However, they provide limited spatial resolution and sophisticated upscaling algorithms are sought after. In this paper, we present a sensor fusion approach to time-of-flight super resolution, based on the combination of depth and texture sources. Unlike other texture guided approaches, we interpret the depth upscaling process as a weighted energy optimization problem. Three different weights are introduced, employing different available sensor data. The individual weights address object boundaries in depth, depth sensor noise, and temporal consistency. Applied in consecutive order, they form three weighting strategies for time-of-flight super resolution. Objective evaluations show advantages in depth accuracy and for depth image based rendering compared with state-of-the-art depth upscaling. Subjective view synthesis evaluation shows a significant increase in viewer preference by a factor of four in stereoscopic viewing conditions. To the best of our knowledge, this is the first extensive subjective test performed on time-of-flight depth upscaling. Objective and subjective results proof the suitability of our approach to time-of-flight super resolution approach for depth scenery capture.

Hierarchical Spatio-temporal Visual Analysis of Cluster Evolution in Electrocorticography Data

DOE PAGES

Murugesan, Sugeerth; Bouchard, Kristofer; Chang, Edward; ...

2016-10-02

Here, we present ECoG ClusterFlow, a novel interactive visual analysis tool for the exploration of high-resolution Electrocorticography (ECoG) data. Our system detects and visualizes dynamic high-level structures, such as communities, using the time-varying spatial connectivity network derived from the high-resolution ECoG data. ECoG ClusterFlow provides a multi-scale visualization of the spatio-temporal patterns underlying the time-varying communities using two views: 1) an overview summarizing the evolution of clusters over time and 2) a hierarchical glyph-based technique that uses data aggregation and small multiples techniques to visualize the propagation of clusters in their spatial domain. ECoG ClusterFlow makes it possible 1) tomore » compare the spatio-temporal evolution patterns across various time intervals, 2) to compare the temporal information at varying levels of granularity, and 3) to investigate the evolution of spatial patterns without occluding the spatial context information. Lastly, we present case studies done in collaboration with neuroscientists on our team for both simulated and real epileptic seizure data aimed at evaluating the effectiveness of our approach.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Miller, Brian W.; Frost, Sophia; Frayo, Shani

Abstract Alpha emitting radionuclides exhibit a potential advantage for cancer treatments because they release large amounts of ionizing energy over a few cell diameters (50–80 μm) causing localized, irreparable double-strand DNA breaks that lead to cell death. Radioimmunotherapy (RIT) approaches using monoclonal antibodies labeled with alpha emitters may inactivate targeted cells with minimal radiation damage to surrounding tissues. For accurate dosimetry in alpha-RIT, tools are needed to visualize and quantify the radioactivity distribution and absorbed dose to targeted and non-targeted cells, especially for organs and tumors with heterogeneous radionuclide distributions. The aim of this study was to evaluate and characterizemore » a novel single-particle digital autoradiography imager, iQID (ionizing-radiation Quantum Imaging Detector), for use in alpha-RIT experiments. Methods: The iQID camera is a scintillator-based radiation detection technology that images and identifies charged-particle and gamma-ray/X-ray emissions spatially and temporally on an event-by-event basis. It employs recent advances in CCD/CMOS cameras and computing hardware for real-time imaging and activity quantification of tissue sections, approaching cellular resolutions. In this work, we evaluated this system’s characteristics for alpha particle imaging including measurements of spatial resolution and background count rates at various detector configurations and quantification of activity distributions. The technique was assessed for quantitative imaging of astatine-211 (211At) activity distributions in cryosections of murine and canine tissue samples. Results: The highest spatial resolution was measured at ~20 μm full width at half maximum (FWHM) and the alpha particle background was measured at a rate of (2.6 ± 0.5) × 10–4 cpm/cm2 (40 mm diameter detector area). Simultaneous imaging of multiple tissue sections was performed using a large-area iQID configuration (ø 11.5 cm). Estimation of the 211At activity distribution was demonstrated at mBq/μg levels. Conclusion: Single-particle digital autoradiography of alpha emitters has advantages over traditional autoradiographic techniques in terms of spatial resolution, sensitivity, and activity quantification capability. The system features and characterization results presented in this study show that iQID is a promising technology for microdosimetry, because it provides necessary information for interpreting alpha-RIT outcomes and for predicting the therapeutic efficacy of cell-targeted approaches using alpha emitters.« less

[An effective method for improving the imaging spatial resolution of terahertz time domain spectroscopy system].

PubMed

Zhang, Zeng-yan; Ji, Te; Zhu, Zhi-yong; Zhao, Hong-wei; Chen, Min; Xiao, Ti-qiao; Guo, Zhi

2015-01-01

Terahertz radiation is an electromagnetic radiation in the range between millimeter waves and far infrared. Due to its low energy and non-ionizing characters, THz pulse imaging emerges as a novel tool in many fields, such as material, chemical, biological medicine, and food safety. Limited spatial resolution is a significant restricting factor of terahertz imaging technology. Near field imaging method was proposed to improve the spatial resolution of terahertz system. Submillimeter scale's spauial resolution can be achieved if the income source size is smaller than the wawelength of the incoming source and the source is very close to the sample. But many changes were needed to the traditional terahertz time domain spectroscopy system, and it's very complex to analyze sample's physical parameters through the terahertz signal. A method of inserting a pinhole upstream to the sample was first proposed in this article to improve the spatial resolution of traditional terahertz time domain spectroscopy system. The measured spatial resolution of terahertz time domain spectroscopy system by knife edge method can achieve spatial resolution curves. The moving stage distance between 10 % and 90 Yo of the maximum signals respectively was defined as the, spatial resolution of the system. Imaging spatial resolution of traditional terahertz time domain spectroscopy system was improved dramatically after inserted a pinhole with diameter 0. 5 mm, 2 mm upstream to the sample. Experimental results show that the spatial resolution has been improved from 1. 276 mm to 0. 774 mm, with the increment about 39 %. Though this simple method, the spatial resolution of traditional terahertz time domain spectroscopy system was increased from millimeter scale to submillimeter scale. A pinhole with diameter 1 mm on a polyethylene plate was taken as sample, to terahertz imaging study. The traditional terahertz time domain spectroscopy system and pinhole inserted terahertz time domain spectroscopy system were applied in the imaging experiment respectively. The relative THz-power loss imaging of samples were use in this article. This method generally delivers the best signal to noise ratio in loss images, dispersion effects are cancelled. Terahertz imaging results show that the sample's boundary was more distinct after inserting the pinhole in front of, sample. The results also conform that inserting pinhole in front of sample can improve the imaging spatial resolution effectively. The theoretical analyses of the method which improve the spatial resolution by inserting a pinhole in front of sample were given in this article. The analyses also indicate that the smaller the pinhole size, the longer spatial coherence length of the system, the better spatial resolution of the system. At the same time the terahertz signal will be reduced accordingly. All the experimental results and theoretical analyses indicate that the method of inserting a pinhole in front of sample can improve the spatial resolution of traditional terahertz time domain spectroscopy system effectively, and it will further expand the application of terahertz imaging technology.

Compressive hyperspectral and multispectral imaging fusion

NASA Astrophysics Data System (ADS)

Espitia, Óscar; Castillo, Sergio; Arguello, Henry

2016-05-01

Image fusion is a valuable framework which combines two or more images of the same scene from one or multiple sensors, allowing to improve the resolution of the images and increase the interpretable content. In remote sensing a common fusion problem consists of merging hyperspectral (HS) and multispectral (MS) images that involve large amount of redundant data, which ignores the highly correlated structure of the datacube along the spatial and spectral dimensions. Compressive HS and MS systems compress the spectral data in the acquisition step allowing to reduce the data redundancy by using different sampling patterns. This work presents a compressed HS and MS image fusion approach, which uses a high dimensional joint sparse model. The joint sparse model is formulated by combining HS and MS compressive acquisition models. The high spectral and spatial resolution image is reconstructed by using sparse optimization algorithms. Different fusion spectral image scenarios are used to explore the performance of the proposed scheme. Several simulations with synthetic and real datacubes show promising results as the reliable reconstruction of a high spectral and spatial resolution image can be achieved by using as few as just the 50% of the datacube.

EIT image reconstruction with four dimensional regularization.

PubMed

Dai, Tao; Soleimani, Manuchehr; Adler, Andy

2008-09-01

Electrical impedance tomography (EIT) reconstructs internal impedance images of the body from electrical measurements on body surface. The temporal resolution of EIT data can be very high, although the spatial resolution of the images is relatively low. Most EIT reconstruction algorithms calculate images from data frames independently, although data are actually highly correlated especially in high speed EIT systems. This paper proposes a 4-D EIT image reconstruction for functional EIT. The new approach is developed to directly use prior models of the temporal correlations among images and 3-D spatial correlations among image elements. A fast algorithm is also developed to reconstruct the regularized images. Image reconstruction is posed in terms of an augmented image and measurement vector which are concatenated from a specific number of previous and future frames. The reconstruction is then based on an augmented regularization matrix which reflects the a priori constraints on temporal and 3-D spatial correlations of image elements. A temporal factor reflecting the relative strength of the image correlation is objectively calculated from measurement data. Results show that image reconstruction models which account for inter-element correlations, in both space and time, show improved resolution and noise performance, in comparison to simpler image models.

Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users

PubMed Central

Calera, Alfonso; Campos, Isidro; Osann, Anna; D’Urso, Guido; Menenti, Massimo

2017-01-01

The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution. This information is greatly appreciated by the end users, i.e., professional farmers or decision-makers, and can be provided in an easy-to-use manner and in near-real-time by using the improvements achieved in web-GIS methodologies (Geographic Information Systems based on web technologies). This paper reviews the most operational and explored methods based on optical remote sensing for the assessment of crop water requirements, identifying strengths and weaknesses and proposing alternatives to advance towards full operational application of this methodology. In addition, we provide a general overview of the tools, which facilitates co-creation and collaboration with stakeholders, paying special attention to these approaches based on web-GIS tools. PMID:28492515

Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users.

PubMed

Calera, Alfonso; Campos, Isidro; Osann, Anna; D'Urso, Guido; Menenti, Massimo

2017-05-11

The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution. This information is greatly appreciated by the end users, i.e., professional farmers or decision-makers, and can be provided in an easy-to-use manner and in near-real-time by using the improvements achieved in web-GIS methodologies (Geographic Information Systems based on web technologies). This paper reviews the most operational and explored methods based on optical remote sensing for the assessment of crop water requirements, identifying strengths and weaknesses and proposing alternatives to advance towards full operational application of this methodology. In addition, we provide a general overview of the tools, which facilitates co-creation and collaboration with stakeholders, paying special attention to these approaches based on web-GIS tools.

On the Reliability of Individual Brain Activity Networks.

PubMed

Cassidy, Ben; Bowman, F DuBois; Rae, Caroline; Solo, Victor

2018-02-01

There is intense interest in fMRI research on whole-brain functional connectivity, and however, two fundamental issues are still unresolved: the impact of spatiotemporal data resolution (spatial parcellation and temporal sampling) and the impact of the network construction method on the reliability of functional brain networks. In particular, the impact of spatiotemporal data resolution on the resulting connectivity findings has not been sufficiently investigated. In fact, a number of studies have already observed that functional networks often give different conclusions across different parcellation scales. If the interpretations from functional networks are inconsistent across spatiotemporal scales, then the whole validity of the functional network paradigm is called into question. This paper investigates the consistency of resting state network structure when using different temporal sampling or spatial parcellation, or different methods for constructing the networks. To pursue this, we develop a novel network comparison framework based on persistent homology from a topological data analysis. We use the new network comparison tools to characterize the spatial and temporal scales under which consistent functional networks can be constructed. The methods are illustrated on Human Connectome Project data, showing that the DISCOH 2 network construction method outperforms other approaches at most data spatiotemporal resolutions.

Introduction of digital soil mapping techniques for the nationwide regionalization of soil condition in Hungary; the first results of the DOSoReMI.hu (Digital, Optimized, Soil Related Maps and Information in Hungary) project

NASA Astrophysics Data System (ADS)

Pásztor, László; Laborczi, Annamária; Szatmári, Gábor; Takács, Katalin; Bakacsi, Zsófia; Szabó, József; Dobos, Endre

2014-05-01

Due to the former soil surveys and mapping activities significant amount of soil information has accumulated in Hungary. Present soil data requirements are mainly fulfilled with these available datasets either by their direct usage or after certain specific and generally fortuitous, thematic and/or spatial inference. Due to the more and more frequently emerging discrepancies between the available and the expected data, there might be notable imperfection as for the accuracy and reliability of the delivered products. With a recently started project (DOSoReMI.hu; Digital, Optimized, Soil Related Maps and Information in Hungary) we would like to significantly extend the potential, how countrywide soil information requirements could be satisfied in Hungary. We started to compile digital soil related maps which fulfil optimally the national and international demands from points of view of thematic, spatial and temporal accuracy. The spatial resolution of the targeted countrywide, digital, thematic maps is at least 1:50.000 (approx. 50-100 meter raster resolution). DOSoReMI.hu results are also planned to contribute to the European part of GSM.net products. In addition to the auxiliary, spatial data themes related to soil forming factors and/or to indicative environmental elements we heavily lean on the various national soil databases. The set of the applied digital soil mapping techniques is gradually broadened incorporating and eventually integrating geostatistical, data mining and GIS tools. In our paper we will present the first results. - Regression kriging (RK) has been used for the spatial inference of certain quantitative data, like particle size distribution components, rootable depth and organic matter content. In the course of RK-based mapping spatially segmented categorical information provided by the SMUs of Digital Kreybig Soil Information System (DKSIS) has been also used in the form of indicator variables. - Classification and regression trees (CART) were used to improve the spatial resolution of category-type soil maps (thematic downscaling), like genetic soil type and soil productivity maps. The approach was justified by the fact that certain thematic soil maps are not available in the required scale. Decision trees were applied for the understanding of the soil-landscape models involved in existing soil maps, and for the post-formalization of survey/compilation rules. The relationships identified and expressed in decision rules made the creation of spatially refined maps possible with the aid of high resolution environmental auxiliary variables. Among these co-variables, a special role was played by larger scale spatial soil information with diverse attributes. As a next step, the testing of random forests for the same purposes has been started. - Due to the simultaneous richness of available Hungarian legacy soil data, spatial inference methods and auxiliary environmental information, there is a high versatility of possible approaches for the compilation of a given soil (related) map. This suggests the opportunity of optimization. For the creation of an object specific soil (related) map with predefined parameters (resolution, accuracy, reliability etc.) one might intend to identify the optimum set of soil data, method and auxiliary co-variables optimized for the resources (data costs, computation requirements etc.). The first findings on the inclusion and joint usage of spatial soil data as well as on the consistency of various evaluations of the result maps will be also presented. Acknowledgement: Our work has been supported by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).

Disaggregation of remotely sensed soil moisture under all sky condition using machine learning approach in Northeast Asia

NASA Astrophysics Data System (ADS)

Kim, S.; Kim, H.; Choi, M.; Kim, K.

2016-12-01

Estimating spatiotemporal variation of soil moisture is crucial to hydrological applications such as flood, drought, and near real-time climate forecasting. Recent advances in space-based passive microwave measurements allow the frequent monitoring of the surface soil moisture at a global scale and downscaling approaches have been applied to improve the spatial resolution of passive microwave products available at local scale applications. However, most downscaling methods using optical and thermal dataset, are valid only in cloud-free conditions; thus renewed downscaling method under all sky condition is necessary for the establishment of spatiotemporal continuity of datasets at fine resolution. In present study Support Vector Machine (SVM) technique was utilized to downscale a satellite-based soil moisture retrievals. The 0.1 and 0.25-degree resolution of daily Land Parameter Retrieval Model (LPRM) L3 soil moisture datasets from Advanced Microwave Scanning Radiometer 2 (AMSR2) were disaggregated over Northeast Asia in 2015. Optically derived estimates of surface temperature (LST), normalized difference vegetation index (NDVI), and its cloud products were obtained from MODerate Resolution Imaging Spectroradiometer (MODIS) for the purpose of downscaling soil moisture in finer resolution under all sky condition. Furthermore, a comparison analysis between in situ and downscaled soil moisture products was also conducted for quantitatively assessing its accuracy. Results showed that downscaled soil moisture under all sky condition not only preserves the quality of AMSR2 LPRM soil moisture at 1km resolution, but also attains higher spatial data coverage. From this research we expect that time continuous monitoring of soil moisture at fine scale regardless of weather conditions would be available.

Spatiotemporal Filtering Using Principal Component Analysis and Karhunen-Loeve Expansion Approaches for Regional GPS Network Analysis

NASA Technical Reports Server (NTRS)

Dong, D.; Fang, P.; Bock, F.; Webb, F.; Prawirondirdjo, L.; Kedar, S.; Jamason, P.

2006-01-01

Spatial filtering is an effective way to improve the precision of coordinate time series for regional GPS networks by reducing so-called common mode errors, thereby providing better resolution for detecting weak or transient deformation signals. The commonly used approach to regional filtering assumes that the common mode error is spatially uniform, which is a good approximation for networks of hundreds of kilometers extent, but breaks down as the spatial extent increases. A more rigorous approach should remove the assumption of spatially uniform distribution and let the data themselves reveal the spatial distribution of the common mode error. The principal component analysis (PCA) and the Karhunen-Loeve expansion (KLE) both decompose network time series into a set of temporally varying modes and their spatial responses. Therefore they provide a mathematical framework to perform spatiotemporal filtering.We apply the combination of PCA and KLE to daily station coordinate time series of the Southern California Integrated GPS Network (SCIGN) for the period 2000 to 2004. We demonstrate that spatially and temporally correlated common mode errors are the dominant error source in daily GPS solutions. The spatial characteristics of the common mode errors are close to uniform for all east, north, and vertical components, which implies a very long wavelength source for the common mode errors, compared to the spatial extent of the GPS network in southern California. Furthermore, the common mode errors exhibit temporally nonrandom patterns.

Light propagation and interaction observed with electrons.

PubMed

Word, Robert C; Fitzgerald, J P S; Könenkamp, R

2016-01-01

We discuss possibilities for a microscopic optical characterization of thin films and surfaces based on photoemission electron microscopy. We show that propagating light with wavelengths across the visible range can readily be visualized, and linear and non-linear materials properties can be evaluated non-invasively with nanometer spatial resolution. While femtosecond temporal resolution can be achieved in pump-probe-type experiments, the interferometric approach presented here has typical image frame times of ~200 fs. Copyright © 2015 Elsevier B.V. All rights reserved.

Dynamic microscale temperature gradient in a gold nanorod solution measured by diffraction-limited nanothermometry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Chengmingyue; Gan, Xiaosong; Li, Xiangping

2015-09-21

We quantify the dynamic microscale temperature gradient in a gold nanorod solution using quantum-dot-based microscopic fluorescence nanothermometry. By incorporating CdSe quantum dots into the solution as a nanothermometer, precise temperature mapping with diffraction-limited spatial resolution and sub-degree temperature resolution is achieved. The acquired data on heat generation and dissipation show an excellent agreement with theoretical simulations. This work reveals an effective approach for noninvasive temperature regulation with localized nanoheaters in microfluidic environment.

Tactile Recognition and Localization Using Object Models: The Case of Polyhedra on a Plane.

DTIC Science & Technology

1983-03-01

poor force resolution, but high spatial resolution. We feel that the viability of this recognition approach has important implications on the design of...of the touched object: 1. Surface point - On the basis of sensor readings, some points on the sensor can be identified as being in contact with...the sensor’s shape and location in space are known, one can determine the position of some point on the touched object, to within some uncertainty

An Updating System for the Gridded Population Database of China Based on Remote Sensing, GIS and Spatial Database Technologies.

PubMed

Yang, Xiaohuan; Huang, Yaohuan; Dong, Pinliang; Jiang, Dong; Liu, Honghui

2009-01-01

The spatial distribution of population is closely related to land use and land cover (LULC) patterns on both regional and global scales. Population can be redistributed onto geo-referenced square grids according to this relation. In the past decades, various approaches to monitoring LULC using remote sensing and Geographic Information Systems (GIS) have been developed, which makes it possible for efficient updating of geo-referenced population data. A Spatial Population Updating System (SPUS) is developed for updating the gridded population database of China based on remote sensing, GIS and spatial database technologies, with a spatial resolution of 1 km by 1 km. The SPUS can process standard Moderate Resolution Imaging Spectroradiometer (MODIS L1B) data integrated with a Pattern Decomposition Method (PDM) and an LULC-Conversion Model to obtain patterns of land use and land cover, and provide input parameters for a Population Spatialization Model (PSM). The PSM embedded in SPUS is used for generating 1 km by 1 km gridded population data in each population distribution region based on natural and socio-economic variables. Validation results from finer township-level census data of Yishui County suggest that the gridded population database produced by the SPUS is reliable.

A New Hybrid Spatio-temporal Model for Estimating Daily Multi-year PM2.5 Concentrations Across Northeastern USA Using High Resolution Aerosol Optical Depth Data

NASA Technical Reports Server (NTRS)

Kloog, Itai; Chudnovsky, Alexandra A.; Just, Allan C.; Nordio, Francesco; Koutrakis, Petros; Coull, Brent A.; Lyapustin, Alexei; Wang, Yujie; Schwartz, Joel

2014-01-01

The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter PM(sub 2.5) for epidemiology studies has increased substantially over the past few years. These recent studies often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data. We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data.We developed and cross validated models to predict daily PM(sub 2.5) at a 1X 1 km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003-2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1 X 1 km grid predictions. We used mixed models regressing PM(sub 2.5) measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site. Our model performance was excellent (mean out-of-sample R(sup 2) = 0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R(sup 2) = 0.87, R(sup)2 = 0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99). Our daily model results show high predictive accuracy at high spatial resolutions and will be useful in reconstructing exposure histories for epidemiological studies across this region.

A New Hybrid Spatio-Temporal Model For Estimating Daily Multi-Year PM2.5 Concentrations Across Northeastern USA Using High Resolution Aerosol Optical Depth Data.

PubMed

Kloog, Itai; Chudnovsky, Alexandra A; Just, Allan C; Nordio, Francesco; Koutrakis, Petros; Coull, Brent A; Lyapustin, Alexei; Wang, Yujie; Schwartz, Joel

2014-10-01

The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter (PM 2.5 ) for epidemiology studies has increased substantially over the past few years. These recent studies often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data. We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data. We developed and cross validated models to predict daily PM 2.5 at a 1×1km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003-2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1×1 km grid predictions. We used mixed models regressing PM 2.5 measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site. Our model performance was excellent (mean out-of-sample R 2 =0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R 2 =0.87, R 2 =0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99). Our daily model results show high predictive accuracy at high spatial resolutions and will be useful in reconstructing exposure histories for epidemiological studies across this region.

SYMPOSIUM ON MULTIMODALITY CARDIOVASCULAR MOLECULAR IMAGING IMAGING TECHNOLOGY - PART 2

PubMed Central

de Kemp, Robert A.; Epstein, Frederick H.; Catana, Ciprian; Tsui, Benjamin M.W.; Ritman, Erik L.

2013-01-01

Rationale The ability to trace or identify specific molecules within a specific anatomic location provides insight into metabolic pathways, tissue components and tracing of solute transport mechanisms. With the increasing use of small animals for research such imaging must have sufficiently high spatial resolution to allow anatomic localization as well as sufficient specificity and sensitivity to provide an accurate description of the molecular distribution and concentration. Methods Imaging methods based on electromagnetic radiation, such as PET, SPECT, MRI and CT, are increasingly applicable due to recent advances in novel scanner hardware, image reconstruction software and availability of novel molecules which have enhanced sensitivity in these methodologies. Results Micro-PET has been advanced by development of detector arrays that provide higher resolution and positron emitting elements that allow new molecular tracers to be labeled. Micro-MRI has been improved in terms of spatial resolution and sensitivity by increased magnet field strength and development of special purpose coils and associated scan protocols. Of particular interest is the associated ability to image local mechanical function and solute transport processes which can be directly related to the molecular information. This is further strengthened by the synergistic integration of the PET with MRI. Micro-SPECT has been improved by use of coded aperture imaging approaches as well as image reconstruction algorithms which can better deal with the photon limited scan data. The limited spatial resolution can be partially overcome by integrating the SPECT with CT. Micro-CT by itself provides exquisite spatial resolution of anatomy, but recent developments of high spatial resolution photon counting and spectrally-sensitive imaging arrays, combined with x-ray optical devices, have promise for actual molecular identification by virtue of the chemical bond lengths of molecules, especially of bio-polymers. Conclusion With the increasing use of small animals for evaluating new clinical imaging techniques as well as providing increased insights into patho-physiological phenomena, the availability of improved detection systems, scanning protocols and associated software, the repertoire of molecular imaging is greatly increased in sensitivity and specificity. PMID:20457793

A New Hybrid Spatio-Temporal Model For Estimating Daily Multi-Year PM2.5 Concentrations Across Northeastern USA Using High Resolution Aerosol Optical Depth Data

PubMed Central

Kloog, Itai; Chudnovsky, Alexandra A.; Just, Allan C.; Nordio, Francesco; Koutrakis, Petros; Coull, Brent A.; Lyapustin, Alexei; Wang, Yujie; Schwartz, Joel

2017-01-01

Background The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter (PM2.5) for epidemiology studies has increased substantially over the past few years. These recent studies often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data. Methods We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data. We developed and cross validated models to predict daily PM2.5 at a 1×1km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003–2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1×1 km grid predictions. We used mixed models regressing PM2.5 measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site. Results Our model performance was excellent (mean out-of-sample R2=0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R2=0.87, R2=0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99). Conclusion Our daily model results show high predictive accuracy at high spatial resolutions and will be useful in reconstructing exposure histories for epidemiological studies across this region. PMID:28966552

High-resolution land cover classification using low resolution global data

NASA Astrophysics Data System (ADS)

Carlotto, Mark J.

2013-05-01

A fusion approach is described that combines texture features from high-resolution panchromatic imagery with land cover statistics derived from co-registered low-resolution global databases to obtain high-resolution land cover maps. The method does not require training data or any human intervention. We use an MxN Gabor filter bank consisting of M=16 oriented bandpass filters (0-180°) at N resolutions (3-24 meters/pixel). The size range of these spatial filters is consistent with the typical scale of manmade objects and patterns of cultural activity in imagery. Clustering reduces the complexity of the data by combining pixels that have similar texture into clusters (regions). Texture classification assigns a vector of class likelihoods to each cluster based on its textural properties. Classification is unsupervised and accomplished using a bank of texture anomaly detectors. Class likelihoods are modulated by land cover statistics derived from lower resolution global data over the scene. Preliminary results from a number of Quickbird scenes show our approach is able to classify general land cover features such as roads, built up area, forests, open areas, and bodies of water over a wide range of scenes.

Towards Continuity in Cloud Properties from MODIS and Suomi-NPP Polar-Orbiting Sensors

NASA Astrophysics Data System (ADS)

Baum, B. A.; Menzel, P.; Gladkova, I.; Heidinger, A. K.

2015-12-01

The intent of this talk is to discuss the progress and issues involved with developing a continuous record of cloud properties since 1978, beginning with the High Resolution Infrared Radiation Sounder (HIRS), then MODIS on the NASA Terra/Aqua platforms, and into the future from merged CrIS and VIIRS data. The MODIS measurements include infrared (IR) window radiances at 8.5-, 11- and 12-μm and four 15-μm channels in the broad CO2 absorption band. Cloud top pressure/height and emissivity are derived using a technique in which the strength is in retrievals for mid-to-high clouds but less so for low clouds where there is little thermal contrast with the surface. Additionally, MODIS provides a decadal IR cloud phase product. The goal now is to extend this continuity from HIRS and MODIS to the S-NPP era. However, there is one large drawback to consider: VIIRS has no infrared (IR) absorption channels. The lack of at least one IR absorption channel on VIIRS degrades the accuracy of the cloud properties. There is a solution: we can construct a 13.3-μm channel from a combination of VIIRS and CrIS (Cross-track Infrared Sounder). The approach involves using the high spatial resolution VIIRS IR window channels in combination with a lower spatial resolution 13.3-μm channel derived using CrIS high spectral resolution measurements. The result is a 13.3-μm pseudo-channel at the VIIRS pixel spatial resolution of 750 m (i.e., M-band resolution). The radiometric accuracy of this approach was tested using MODIS and AIRS, and found to be within 1-2%. The availability of the pseudo-channel increases the potential for achieving continuity between MODIS and S-NPP. Since future platforms will likely continue with a pairing of an imager and hyperspectral sounder, this work lays a foundation for future cloud product continuity. We will show how the use of this new channel will impact the cloud height and phase products.

Spatiotemporal comparison of highly-resolved emissions and concentrations of carbon dioxide and criteria pollutants in Salt Lake City, Utah for health and policy applications

NASA Astrophysics Data System (ADS)

Mendoza, D. L.; Lin, J. C.; Mitchell, L.; Gurney, K. R.; Patarasuk, R.; Fasoli, B.; Bares, R.; o'Keefe, D.; Song, T.; Huang, J.; Horel, J.; Crosman, E.; Ehleringer, J. R.

2015-12-01

This study addresses the need for robust highly-resolved emissions and concentration data required for planning purposes and policy development aimed at managing pollutant sources. Adverse health effects resulting from urban pollution exposure are dependent on proximity to emission sources and atmospheric mixing, necessitating models with high spatial and temporal resolution. As urban emission sources co-emit carbon dioxide (CO2) and criteria pollutants (CAPs), efforts to reduce specific pollutants would synergistically reduce others. We present emissions inventories and modeled concentrations for CO2 and CAPs: carbon monoxide (CO), lead (Pb), nitrogen oxides (NOx), particulate matter (PM2.5 and PM10), and sulfur oxides (SOx) for Salt Lake County, Utah. We compare the resulting concentrations against stationary and mobile measurement data and present a systematic quantification of uncertainties. The emissions inventory for CO2 is based on the Hestia emissions data inventory that resolves emissions at an hourly, building and road link resolution as well as hourly gridded emissions with a 0.002o x 0.002o spatial resolution. Two methods for deriving criteria pollutant emission inventories were compared. One was constructed using methods similar to Hestia but downscales total emissions based on the 2011 National Emissions Inventory (NEI). The other used Emission Modeling Clearinghouse spatial and temporal surrogates to downscale the NEI data from annual and county-level resolution to hourly and 0.002o x 0.002o grid cells. The gridded emissions from both criteria pollutant methods were compared against the Hestia CO2 gridded data to characterize spatial similarities and differences between them. Correlations were calculated at multiple scales of aggregation. The CALPUFF dispersion model was used to transport emissions and estimate air pollutant concentrations at an hourly 0.002o x 0.002o resolution. The resulting concentrations were spatially compared in the same manner as the emissions. Modeled results were compared against stationary measurements and from equipment mounted atop a light rail car in the Salt Lake City area. The comparison between both approaches to emissions estimation and resulting concentrations highlights spatial locations and hours of high variability and uncertainty.

Additional studies of forest classification accuracy as influenced by multispectral scanner spatial resolution

NASA Technical Reports Server (NTRS)

Sadowski, F. E.; Sarno, J. E.

1976-01-01

First, an analysis of forest feature signatures was used to help explain the large variation in classification accuracy that can occur among individual forest features for any one case of spatial resolution and the inconsistent changes in classification accuracy that were demonstrated among features as spatial resolution was degraded. Second, the classification rejection threshold was varied in an effort to reduce the large proportion of unclassified resolution elements that previously appeared in the processing of coarse resolution data when a constant rejection threshold was used for all cases of spatial resolution. For the signature analysis, two-channel ellipse plots showing the feature signature distributions for several cases of spatial resolution indicated that the capability of signatures to correctly identify their respective features is dependent on the amount of statistical overlap among signatures. Reductions in signature variance that occur in data of degraded spatial resolution may not necessarily decrease the amount of statistical overlap among signatures having large variance and small mean separations. Features classified by such signatures may thus continue to have similar amounts of misclassified elements in coarser resolution data, and thus, not necessarily improve in classification accuracy.

Characterizing bio-optical and ecological features of algal bloom waters for detection and tracking from space

NASA Astrophysics Data System (ADS)

Ahmed, S.; Amin, R.; Gladkova, I.; Gilerson, A.; Grossberg, M.; Hlaing, S.; Shariar, F.; Alabi, P.

2010-04-01

The detection and monitoring of harmful algal blooms using in-situ field measurements is both labor intensive and is practically limited on achievable temporal and spatial resolutions, since field measurements are typically carried out at a series of discrete points and at discrete times, with practical limitations on temporal continuity. The planning and preparation of remedial measures to reduce health risks, etc., requires detection approaches which can effectively cover larger areas with contiguous spatial resolutions, and at the same time offer a more comprehensive and contemporaneous snapshot of entire blooms as they occur. This is beyond capabilities of in-situ measurements and it is in this context that satellite Ocean Color sensors offer potential advantages for bloom detection and monitoring. In this paper we examine the applications and limitations of an approach we have recently developed for the detection of K. brevis blooms from satellite Ocean Color Sensors measurements, the Red Band Difference Technique, and compare it to other detection algorithm approaches, including a new statistical based approach also proposed here. To achieve more uniform standards of comparisons, the performance of different techniques for detection are applied to the same specific verified blooms occurring off the West Florida Shelf (WFS) that have been verified by in-situ measurements.

Handling Different Spatial Resolutions in Image Fusion by Multivariate Curve Resolution-Alternating Least Squares for Incomplete Image Multisets.

PubMed

Piqueras, Sara; Bedia, Carmen; Beleites, Claudia; Krafft, Christoph; Popp, Jürgen; Maeder, Marcel; Tauler, Romà; de Juan, Anna

2018-06-05

Data fusion of different imaging techniques allows a comprehensive description of chemical and biological systems. Yet, joining images acquired with different spectroscopic platforms is complex because of the different sample orientation and image spatial resolution. Whereas matching sample orientation is often solved by performing suitable affine transformations of rotation, translation, and scaling among images, the main difficulty in image fusion is preserving the spatial detail of the highest spatial resolution image during multitechnique image analysis. In this work, a special variant of the unmixing algorithm Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) for incomplete multisets is proposed to provide a solution for this kind of problem. This algorithm allows analyzing simultaneously images collected with different spectroscopic platforms without losing spatial resolution and ensuring spatial coherence among the images treated. The incomplete multiset structure concatenates images of the two platforms at the lowest spatial resolution with the image acquired with the highest spatial resolution. As a result, the constituents of the sample analyzed are defined by a single set of distribution maps, common to all platforms used and with the highest spatial resolution, and their related extended spectral signatures, covering the signals provided by each of the fused techniques. We demonstrate the potential of the new variant of MCR-ALS for multitechnique analysis on three case studies: (i) a model example of MIR and Raman images of pharmaceutical mixture, (ii) FT-IR and Raman images of palatine tonsil tissue, and (iii) mass spectrometry and Raman images of bean tissue.

Multiple speckle illumination for optical-resolution photoacoustic imaging

NASA Astrophysics Data System (ADS)

Poisson, Florian; Stasio, Nicolino; Moser, Christophe; Psaltis, Demetri; Bossy, Emmanuel

2017-03-01

Optical-resolution photoacoustic microscopy offers exquisite and specific contrast to optical absorption. Conventional approaches generally involves raster scanning a focused spot over the sample. Here, we demonstrate that a full-field illumination approach with multiple speckle illumination can also provide diffraction-limited optical-resolution photoacoustic images. Two different proof-of-concepts are demonstrated with micro-structured test samples. The first approach follows the principle of correlation/ghost imaging,1, 2 and is based on cross-correlating photoacoustic signals under multiple speckle illumination with known speckle patterns measured during a calibration step. The second approach is a speckle scanning microscopy technique, which adapts the technique proposed in fluorescence microscopy by Bertolotti and al.:3 in our work, spatially unresolved photoacoustic measurements are performed for various translations of unknown speckle patterns. A phase-retrieval algorithm is used to reconstruct the object from the knowledge of the modulus of its Fourier Transform yielded by the measurements. Because speckle patterns naturally appear in many various situations, including propagation through biological tissue or multi-mode fibers (for which focusing light is either very demanding if not impossible), speckle-illumination-based photoacoustic microscopy provides a powerful framework for the development of novel reconstruction approaches, well-suited to compressed sensing approaches.2

Calibrated Multi-Temporal Edge Images for City Infrastructure Growth Assessment and Prediction

NASA Astrophysics Data System (ADS)

Al-Ruzouq, R.; Shanableh, A.; Boharoon, Z.; Khalil, M.

2018-03-01

Urban Growth or urbanization can be defined as the gradual process of city's population growth and infrastructure development. It is typically demonstrated by the expansion of a city's infrastructure, mainly development of its roads and buildings. Uncontrolled urban Growth in cities has been responsible for several problems that include living environment, drinking water, noise and air pollution, waste management, traffic congestion and hydraulic processes. Accurate identification of urban growth is of great importance for urban planning and water/land management. Recent advances in satellite imagery, in terms of improved spatial and temporal resolutions, allows for efficient identification of change patterns and the prediction of built-up areas. In this study, two approaches were adapted to quantify and assess the pattern of urbanization, in Ajman City at UAE, during the last three decades. The first approach relies on image processing techniques and multi-temporal Landsat satellite images with ground resolution varying between 15 to 60 meters. In this approach, the derived edge images (roads and buildings) were used as the basis of change detection. The second approach relies on digitizing features from high-resolution images captured at different years. The latest approach was adopted, as a reference and ground truth, to calibrate extracted edges from Landsat images. It has been found that urbanized area almost increased by 12 folds during the period 1975-2015 where the growth of buildings and roads were almost parallel until 2005 when the roads spatial expansion witnessed a steep increase due to the vertical expansion of the City. Extracted Edges features, were successfully used for change detection and quantification in term of buildings and roads.

High-resolution stochastic downscaling of climate models: simulating wind advection, cloud cover and precipitation

NASA Astrophysics Data System (ADS)

Peleg, Nadav; Fatichi, Simone; Burlando, Paolo

2015-04-01

A new stochastic approach to generate wind advection, cloud cover and precipitation fields is presented with the aim of formulating a space-time weather generator characterized by fields with high spatial and temporal resolution (e.g., 1 km x 1 km and 5 min). Its use is suitable for stochastic downscaling of climate scenarios in the context of hydrological, ecological and geomorphological applications. The approach is based on concepts from the Advanced WEather GENerator (AWE-GEN) presented by Fatichi et al. (2011, Adv. Water Resour.), the Space-Time Realizations of Areal Precipitation model (STREAP) introduced by Paschalis et al. (2013, Water Resour. Res.), and the High-Resolution Synoptically conditioned Weather Generator (HiReS-WG) presented by Peleg and Morin (2014, Water Resour. Res.). Advection fields are generated on the basis of the 500 hPa u and v wind direction variables derived from global or regional climate models. The advection velocity and direction are parameterized using Kappa and von Mises distributions respectively. A random Gaussian fields is generated using a fast Fourier transform to preserve the spatial correlation of advection. The cloud cover area, total precipitation area and mean advection of the field are coupled using a multi-autoregressive model. The approach is relatively parsimonious in terms of computational demand and, in the context of climate change, allows generating many stochastic realizations of current and projected climate in a fast and efficient way. A preliminary test of the approach is presented with reference to a case study in a complex orography terrain in the Swiss Alps.

Fine-resolution conservation planning with limited climate-change information.

PubMed

Shah, Payal; Mallory, Mindy L; Ando, Amy W; Guntenspergen, Glenn R

2017-04-01

Climate-change induced uncertainties in future spatial patterns of conservation-related outcomes make it difficult to implement standard conservation-planning paradigms. A recent study translates Markowitz's risk-diversification strategy from finance to conservation settings, enabling conservation agents to use this diversification strategy for allocating conservation and restoration investments across space to minimize the risk associated with such uncertainty. However, this method is information intensive and requires a large number of forecasts of ecological outcomes associated with possible climate-change scenarios for carrying out fine-resolution conservation planning. We developed a technique for iterative, spatial portfolio analysis that can be used to allocate scarce conservation resources across a desired level of subregions in a planning landscape in the absence of a sufficient number of ecological forecasts. We applied our technique to the Prairie Pothole Region in central North America. A lack of sufficient future climate information prevented attainment of the most efficient risk-return conservation outcomes in the Prairie Pothole Region. The difference in expected conservation returns between conservation planning with limited climate-change information and full climate-change information was as large as 30% for the Prairie Pothole Region even when the most efficient iterative approach was used. However, our iterative approach allowed finer resolution portfolio allocation with limited climate-change forecasts such that the best possible risk-return combinations were obtained. With our most efficient iterative approach, the expected loss in conservation outcomes owing to limited climate-change information could be reduced by 17% relative to other iterative approaches. © 2016 Society for Conservation Biology.

An optical systems analysis approach to image resampling

NASA Technical Reports Server (NTRS)

Lyon, Richard G.

1997-01-01

All types of image registration require some type of resampling, either during the registration or as a final step in the registration process. Thus the image(s) must be regridded into a spatially uniform, or angularly uniform, coordinate system with some pre-defined resolution. Frequently the ending resolution is not the resolution at which the data was observed with. The registration algorithm designer and end product user are presented with a multitude of possible resampling methods each of which modify the spatial frequency content of the data in some way. The purpose of this paper is threefold: (1) to show how an imaging system modifies the scene from an end to end optical systems analysis approach, (2) to develop a generalized resampling model, and (3) empirically apply the model to simulated radiometric scene data and tabulate the results. A Hanning windowed sinc interpolator method will be developed based upon the optical characterization of the system. It will be discussed in terms of the effects and limitations of sampling, aliasing, spectral leakage, and computational complexity. Simulated radiometric scene data will be used to demonstrate each of the algorithms. A high resolution scene will be "grown" using a fractal growth algorithm based on mid-point recursion techniques. The result scene data will be convolved with a point spread function representing the optical response. The resultant scene will be convolved with the detection systems response and subsampled to the desired resolution. The resultant data product will be subsequently resampled to the correct grid using the Hanning windowed sinc interpolator and the results and errors tabulated and discussed.

Technique development of 3D dynamic CS-EPSI for hyperpolarized 13 C pyruvate MR molecular imaging of human prostate cancer.

PubMed

Chen, Hsin-Yu; Larson, Peder E Z; Gordon, Jeremy W; Bok, Robert A; Ferrone, Marcus; van Criekinge, Mark; Carvajal, Lucas; Cao, Peng; Pauly, John M; Kerr, Adam B; Park, Ilwoo; Slater, James B; Nelson, Sarah J; Munster, Pamela N; Aggarwal, Rahul; Kurhanewicz, John; Vigneron, Daniel B

2018-03-25

The purpose of this study was to develop a new 3D dynamic carbon-13 compressed sensing echoplanar spectroscopic imaging (EPSI) MR sequence and test it in phantoms, animal models, and then in prostate cancer patients to image the metabolic conversion of hyperpolarized [1- 13 C]pyruvate to [1- 13 C]lactate with whole gland coverage at high spatial and temporal resolution. A 3D dynamic compressed sensing (CS)-EPSI sequence with spectral-spatial excitation was designed to meet the required spatial coverage, time and spatial resolution, and RF limitations of the 3T MR scanner for its clinical translation for prostate cancer patient imaging. After phantom testing, animal studies were performed in rats and transgenic mice with prostate cancers. For patient studies, a GE SPINlab polarizer (GE Healthcare, Waukesha, WI) was used to produce hyperpolarized sterile GMP [1- 13 C]pyruvate. 3D dynamic 13 C CS-EPSI data were acquired starting 5 s after injection throughout the gland with a spatial resolution of 0.5 cm 3 , 18 time frames, 2-s temporal resolution, and 36 s total acquisition time. Through preclinical testing, the 3D CS-EPSI sequence developed in this project was shown to provide the desired spectral, temporal, and spatial 5D HP 13 C MR data. In human studies, the 3D dynamic HP CS-EPSI approach provided first-ever simultaneously volumetric and dynamic images of the LDH-catalyzed conversion of [1- 13 C]pyruvate to [1- 13 C]lactate in a biopsy-proven prostate cancer patient with full gland coverage. The results demonstrate the feasibility to characterize prostate cancer metabolism in animals, and now patients using this new 3D dynamic HP MR technique to measure k PL , the kinetic rate constant of [1- 13 C]pyruvate to [1- 13 C]lactate conversion. © 2018 International Society for Magnetic Resonance in Medicine.

Accelerated damage visualization using binary search with fixed pitch-catch distance laser ultrasonic scanning

NASA Astrophysics Data System (ADS)

Park, Byeongjin; Sohn, Hoon

2017-07-01

Laser ultrasonic scanning, especially full-field wave propagation imaging, is attractive for damage visualization thanks to its noncontact nature, sensitivity to local damage, and high spatial resolution. However, its practicality is limited because scanning at a high spatial resolution demands a prohibitively long scanning time. Inspired by binary search, an accelerated damage visualization technique is developed to visualize damage with a reduced scanning time. The pitch-catch distance between the excitation point and the sensing point is also fixed during scanning to maintain a high signal-to-noise ratio (SNR) of measured ultrasonic responses. The approximate damage boundary is identified by examining the interactions between ultrasonic waves and damage observed at the scanning points that are sparsely selected by a binary search algorithm. Here, a time-domain laser ultrasonic response is transformed into a spatial ultrasonic domain response using a basis pursuit approach so that the interactions between ultrasonic waves and damage, such as reflections and transmissions, can be better identified in the spatial ultrasonic domain. Then, the area inside the identified damage boundary is visualized as damage. The performance of the proposed damage visualization technique is validated excusing a numerical simulation performed on an aluminum plate with a notch and experiments performed on an aluminum plate with a crack and a wind turbine blade with delamination. The proposed damage visualization technique accelerates the damage visualization process in three aspects: (1) the number of measurements that is necessary for damage visualization is dramatically reduced by a binary search algorithm; (2) the number of averaging that is necessary to achieve a high SNR is reduced by maintaining the wave propagation distance short; and (3) with the proposed technique, the same damage can be identified with a lower spatial resolution than the spatial resolution required by full-field wave propagation imaging.

Assessing the Resolution Adaptability of the Zhang-McFarlane Cumulus Parameterization With Spatial and Temporal Averaging: RESOLUTION ADAPTABILITY OF ZM SCHEME

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yun, Yuxing; Fan, Jiwen; Xiao, Heng

Realistic modeling of cumulus convection at fine model resolutions (a few to a few tens of km) is problematic since it requires the cumulus scheme to adapt to higher resolution than they were originally designed for (~100 km). To solve this problem, we implement the spatial averaging method proposed in Xiao et al. (2015) and also propose a temporal averaging method for the large-scale convective available potential energy (CAPE) tendency in the Zhang-McFarlane (ZM) cumulus parameterization. The resolution adaptability of the original ZM scheme, the scheme with spatial averaging, and the scheme with both spatial and temporal averaging at 4-32more » km resolution is assessed using the Weather Research and Forecasting (WRF) model, by comparing with Cloud Resolving Model (CRM) results. We find that the original ZM scheme has very poor resolution adaptability, with sub-grid convective transport and precipitation increasing significantly as the resolution increases. The spatial averaging method improves the resolution adaptability of the ZM scheme and better conserves the total transport of moist static energy and total precipitation. With the temporal averaging method, the resolution adaptability of the scheme is further improved, with sub-grid convective precipitation becoming smaller than resolved precipitation for resolution higher than 8 km, which is consistent with the results from the CRM simulation. Both the spatial distribution and time series of precipitation are improved with the spatial and temporal averaging methods. The results may be helpful for developing resolution adaptability for other cumulus parameterizations that are based on quasi-equilibrium assumption.« less

Enhanced PET resolution by combining pinhole collimation and coincidence detection

NASA Astrophysics Data System (ADS)

DiFilippo, Frank P.

2015-10-01

Spatial resolution of clinical PET scanners is limited by detector design and photon non-colinearity. Although dedicated small animal PET scanners using specialized high-resolution detectors have been developed, enhancing the spatial resolution of clinical PET scanners is of interest as a more available alternative. Multi-pinhole 511 keV SPECT is capable of high spatial resolution but requires heavily shielded collimators to avoid significant background counts. A practical approach with clinical PET detectors is to combine multi-pinhole collimation with coincidence detection. In this new hybrid modality, there are three locations associated with each event, namely those of the two detected photons and the pinhole aperture. These three locations over-determine the line of response and provide redundant information that is superior to coincidence detection or pinhole collimation alone. Multi-pinhole collimation provides high resolution and avoids non-colinearity error but is subject to collimator penetration and artifacts from overlapping projections. However the coincidence information, though at lower resolution, is valuable for determining whether the photon passed near a pinhole within the cone acceptance angle and for identifying through which pinhole the photon passed. This information allows most photons penetrating through the collimator to be rejected and avoids overlapping projections. With much improved event rejection, a collimator with minimal shielding may be used, and a lightweight add-on collimator for high resolution imaging is feasible for use with a clinical PET scanner. Monte Carlo simulations were performed of a 18F hot rods phantom and a 54-pinhole unfocused whole-body mouse collimator with a clinical PET scanner. Based on coincidence information and pinhole geometry, events were accepted or rejected, and pinhole-specific crystal-map projections were generated. Tomographic images then were reconstructed using a conventional pinhole SPECT algorithm. Hot rods of 1.4 mm diameter were resolved easily in a simulated phantom. System sensitivity was 0.09% for a simulated 70-mm line source corresponding to the NEMA NU-4 mouse phantom. Higher resolution is expected with further optimization of pinhole design, and higher sensitivity is expected with a focused and denser pinhole configuration. The simulations demonstrate high spatial resolution and feasibility of small animal imaging with an add-on multi-pinhole collimator for a clinical PET scanner. Further work is needed to develop geometric calibration and quantitative data corrections and, eventually, to construct a prototype device and produce images with physical phantoms.

Scalable and fast heterogeneous molecular simulation with predictive parallelization schemes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guzman, Horacio V.; Junghans, Christoph; Kremer, Kurt

Multiscale and inhomogeneous molecular systems are challenging topics in the field of molecular simulation. In particular, modeling biological systems in the context of multiscale simulations and exploring material properties are driving a permanent development of new simulation methods and optimization algorithms. In computational terms, those methods require parallelization schemes that make a productive use of computational resources for each simulation and from its genesis. Here, we introduce the heterogeneous domain decomposition approach, which is a combination of an heterogeneity-sensitive spatial domain decomposition with an a priori rearrangement of subdomain walls. Within this approach and paper, the theoretical modeling and scalingmore » laws for the force computation time are proposed and studied as a function of the number of particles and the spatial resolution ratio. We also show the new approach capabilities, by comparing it to both static domain decomposition algorithms and dynamic load-balancing schemes. Specifically, two representative molecular systems have been simulated and compared to the heterogeneous domain decomposition proposed in this work. Finally, these two systems comprise an adaptive resolution simulation of a biomolecule solvated in water and a phase-separated binary Lennard-Jones fluid.« less

Scalable and fast heterogeneous molecular simulation with predictive parallelization schemes

DOE PAGES

Guzman, Horacio V.; Junghans, Christoph; Kremer, Kurt; ...

2017-11-27

Multiscale and inhomogeneous molecular systems are challenging topics in the field of molecular simulation. In particular, modeling biological systems in the context of multiscale simulations and exploring material properties are driving a permanent development of new simulation methods and optimization algorithms. In computational terms, those methods require parallelization schemes that make a productive use of computational resources for each simulation and from its genesis. Here, we introduce the heterogeneous domain decomposition approach, which is a combination of an heterogeneity-sensitive spatial domain decomposition with an a priori rearrangement of subdomain walls. Within this approach and paper, the theoretical modeling and scalingmore » laws for the force computation time are proposed and studied as a function of the number of particles and the spatial resolution ratio. We also show the new approach capabilities, by comparing it to both static domain decomposition algorithms and dynamic load-balancing schemes. Specifically, two representative molecular systems have been simulated and compared to the heterogeneous domain decomposition proposed in this work. Finally, these two systems comprise an adaptive resolution simulation of a biomolecule solvated in water and a phase-separated binary Lennard-Jones fluid.« less

Dem Reconstruction Using Light Field and Bidirectional Reflectance Function from Multi-View High Resolution Spatial Images

NASA Astrophysics Data System (ADS)

de Vieilleville, F.; Ristorcelli, T.; Delvit, J.-M.

2016-06-01

This paper presents a method for dense DSM reconstruction from high resolution, mono sensor, passive imagery, spatial panchromatic image sequence. The interest of our approach is four-fold. Firstly, we extend the core of light field approaches using an explicit BRDF model from the Image Synthesis community which is more realistic than the Lambertian model. The chosen model is the Cook-Torrance BRDF which enables us to model rough surfaces with specular effects using specific material parameters. Secondly, we extend light field approaches for non-pinhole sensors and non-rectilinear motion by using a proper geometric transformation on the image sequence. Thirdly, we produce a 3D volume cost embodying all the tested possible heights and filter it using simple methods such as Volume Cost Filtering or variational optimal methods. We have tested our method on a Pleiades image sequence on various locations with dense urban buildings and report encouraging results with respect to classic multi-label methods such as MIC-MAC, or more recent pipelines such as S2P. Last but not least, our method also produces maps of material parameters on the estimated points, allowing us to simplify building classification or road extraction.

Investigating trends in water use over the Choptank River watershed using a multi-satellite data fusion approach

USDA-ARS?s Scientific Manuscript database

Satellite remote sensing technologies have been widely used to map spatiotemporal variability in consumptive water use (or evapotranspiration; ET) for agricultural water management applications. However, current satellite-based sensors with the high spatial resolution required to map ET at sub-field...

Investigating water use over the Choptank River Watershed using a multi-satellite data fusion approach

USDA-ARS?s Scientific Manuscript database

Satellite remote sensing technologies have been widely used to map spatiotemporal variability in consumptive water use (or evapotranspiration; ET) for agricultural water management applications. However, current satellite-based sensors with the high spatial resolution required to map ET at sub-field...

How does spatial and temporal resolution of vegetation index impact crop yield estimation?

USDA-ARS?s Scientific Manuscript database

Timely and accurate estimation of crop yield before harvest is critical for food market and administrative planning. Remote sensing data have long been used in crop yield estimation for decades. The process-based approach uses light use efficiency model to estimate crop yield. Vegetation index (VI) ...

Statistical model based iterative reconstruction (MBIR) in clinical CT systems. Part II. Experimental assessment of spatial resolution performance.

PubMed

Li, Ke; Garrett, John; Ge, Yongshuai; Chen, Guang-Hong

2014-07-01

Statistical model based iterative reconstruction (MBIR) methods have been introduced to clinical CT systems and are being used in some clinical diagnostic applications. The purpose of this paper is to experimentally assess the unique spatial resolution characteristics of this nonlinear reconstruction method and identify its potential impact on the detectabilities and the associated radiation dose levels for specific imaging tasks. The thoracic section of a pediatric phantom was repeatedly scanned 50 or 100 times using a 64-slice clinical CT scanner at four different dose levels [CTDIvol =4, 8, 12, 16 (mGy)]. Both filtered backprojection (FBP) and MBIR (Veo(®), GE Healthcare, Waukesha, WI) were used for image reconstruction and results were compared with one another. Eight test objects in the phantom with contrast levels ranging from 13 to 1710 HU were used to assess spatial resolution. The axial spatial resolution was quantified with the point spread function (PSF), while the z resolution was quantified with the slice sensitivity profile. Both were measured locally on the test objects and in the image domain. The dependence of spatial resolution on contrast and dose levels was studied. The study also features a systematic investigation of the potential trade-off between spatial resolution and locally defined noise and their joint impact on the overall image quality, which was quantified by the image domain-based channelized Hotelling observer (CHO) detectability index d'. (1) The axial spatial resolution of MBIR depends on both radiation dose level and image contrast level, whereas it is supposedly independent of these two factors in FBP. The axial spatial resolution of MBIR always improved with an increasing radiation dose level and/or contrast level. (2) The axial spatial resolution of MBIR became equivalent to that of FBP at some transitional contrast level, above which MBIR demonstrated superior spatial resolution than FBP (and vice versa); the value of this transitional contrast highly depended on the dose level. (3) The PSFs of MBIR could be approximated as Gaussian functions with reasonably good accuracy. (4) Thez resolution of MBIR showed similar contrast and dose dependence. (5) Noise standard deviation assessed on the edges of objects demonstrated a trade-off with spatial resolution in MBIR. (5) When both spatial resolution and image noise were considered using the CHO analysis, MBIR led to significant improvement in the overall CT image quality for both high and low contrast detection tasks at both standard and low dose levels. Due to the intrinsic nonlinearity of the MBIR method, many well-known CT spatial resolution and noise properties have been modified. In particular, dose dependence and contrast dependence have been introduced to the spatial resolution of CT images by MBIR. The method has also introduced some novel noise-resolution trade-off not seen in traditional CT images. While the benefits of MBIR regarding the overall image quality, as demonstrated in this work, are significant, the optimal use of this method in clinical practice demands a thorough understanding of its unique physical characteristics.

Resolution and quantification accuracy enhancement of functional delay and sum beamforming for three-dimensional acoustic source identification with solid spherical arrays

NASA Astrophysics Data System (ADS)

Chu, Zhigang; Yang, Yang; Shen, Linbang

2017-05-01

Functional delay and sum (FDAS) is a novel beamforming algorithm introduced for the three-dimensional (3D) acoustic source identification with solid spherical microphone arrays. Being capable of offering significantly attenuated sidelobes with a fast speed, the algorithm promises to play an important role in interior acoustic source identification. However, it presents some intrinsic imperfections, specifically poor spatial resolution and low quantification accuracy. This paper focuses on conquering these imperfections by ridge detection (RD) and deconvolution approach for the mapping of acoustic sources (DAMAS). The suggested methods are referred to as FDAS+RD and FDAS+RD+DAMAS. Both computer simulations and experiments are utilized to validate their effects. Several interesting conclusions have emerged: (1) FDAS+RD and FDAS+RD+DAMAS both can dramatically ameliorate FDAS's spatial resolution and at the same time inherit its advantages. (2) Compared to the conventional DAMAS, FDAS+RD+DAMAS enjoys the same super spatial resolution, stronger sidelobe attenuation capability and more than two hundred times faster speed. (3) FDAS+RD+DAMAS can effectively conquer FDAS's low quantification accuracy. Whether the focus distance is equal to the distance from the source to the array center or not, it can quantify the source average pressure contribution accurately. This study will be of great significance to the accurate and quick localization and quantification of acoustic sources in cabin environments.

3D undersampled golden-radial phase encoding for DCE-MRA using inherently regularized iterative SENSE.

PubMed

Prieto, Claudia; Uribe, Sergio; Razavi, Reza; Atkinson, David; Schaeffter, Tobias

2010-08-01

One of the current limitations of dynamic contrast-enhanced MR angiography is the requirement of both high spatial and high temporal resolution. Several undersampling techniques have been proposed to overcome this problem. However, in most of these methods the tradeoff between spatial and temporal resolution is constant for all the time frames and needs to be specified prior to data collection. This is not optimal for dynamic contrast-enhanced MR angiography where the dynamics of the process are difficult to predict and the image quality requirements are changing during the bolus passage. Here, we propose a new highly undersampled approach that allows the retrospective adaptation of the spatial and temporal resolution. The method combines a three-dimensional radial phase encoding trajectory with the golden angle profile order and non-Cartesian Sensitivity Encoding (SENSE) reconstruction. Different regularization images, obtained from the same acquired data, are used to stabilize the non-Cartesian SENSE reconstruction for the different phases of the bolus passage. The feasibility of the proposed method was demonstrated on a numerical phantom and in three-dimensional intracranial dynamic contrast-enhanced MR angiography of healthy volunteers. The acquired data were reconstructed retrospectively with temporal resolutions from 1.2 sec to 8.1 sec, providing a good depiction of small vessels, as well as distinction of different temporal phases.

Monitoring Forest Degradation for a Case Study in Cambodia: Comparison of Landsat 8 and Sentinel-2 Imagery

NASA Astrophysics Data System (ADS)

Langner, Andreas; Miettinen, Jukka; Stibig, Hans-Jurgen

2016-08-01

We use a Normalized Burned Ratio (NBR) differential approach for detecting forest canopy disturbance caused by selective logging in evergreen tropical moist forests of central Cambodia. The general disturbance pattern obtained from Landsat 8 (30 m) imagery is largely compatible to Sentinel-2 (10 m), showing good conformity to high resolution RapidEye reference data. However, the 10 m spatial resolution of Sentinel-2 provides notably higher spatial detail and purer pixel values, increasing the potential for detecting fine and subtle forest canopy changes as indicators for potential forest degradation. We can expect further improvement for detecting short-lived disturbance signals in tropical forest canopies due to an increased revisit frequency (5 days) after the Sentinel-2B launch.

Parameters influencing focalization spot in time reversal of acoustic waves

NASA Astrophysics Data System (ADS)

Zophoniasson, Harald; Bolzmacher, Christian; Hafez, Moustafa

2015-05-01

Time reversal is an approach that can be used to focus acoustic waves in a particular location on a surface, allowing a multitouch tactile feedback interaction. The spatial resolution in this case depends on several parameters, such as geometrical parameters, frequency used and material properties, described by the Lamb wave theory. This paper highlights the impact of frequency, geometrical parameters such as plate thickness and transducer's surface on the focused spot dimensions. In this paper a study of the influence of the plate's thickness and the frequency bandwidth used in the focusing process is presented. It is also shown that the dimension of the piezoelectric diaphragms used has little influence on the spatial resolution. Resonant behavior of the plate and its implication on focus point dimension and focalization contrast were investigated.

Analysis and improvements of Adaptive Particle Refinement (APR) through CPU time, accuracy and robustness considerations

NASA Astrophysics Data System (ADS)

Chiron, L.; Oger, G.; de Leffe, M.; Le Touzé, D.

2018-02-01

While smoothed-particle hydrodynamics (SPH) simulations are usually performed using uniform particle distributions, local particle refinement techniques have been developed to concentrate fine spatial resolutions in identified areas of interest. Although the formalism of this method is relatively easy to implement, its robustness at coarse/fine interfaces can be problematic. Analysis performed in [16] shows that the radius of refined particles should be greater than half the radius of unrefined particles to ensure robustness. In this article, the basics of an Adaptive Particle Refinement (APR) technique, inspired by AMR in mesh-based methods, are presented. This approach ensures robustness with alleviated constraints. Simulations applying the new formalism proposed achieve accuracy comparable to fully refined spatial resolutions, together with robustness, low CPU times and maintained parallel efficiency.

Scaling of surface energy fluxes using remotely sensed data

NASA Astrophysics Data System (ADS)

French, Andrew Nichols

Accurate estimates of evapotranspiration (ET) across multiple terrains would greatly ease challenges faced by hydrologists, climate modelers, and agronomists as they attempt to apply theoretical models to real-world situations. One ET estimation approach uses an energy balance model to interpret a combination of meteorological observations taken at the surface and data captured by remote sensors. However, results of this approach have not been accurate because of poor understanding of the relationship between surface energy flux and land cover heterogeneity, combined with limits in available resolution of remote sensors. The purpose of this study was to determine how land cover and image resolution affect ET estimates. Using remotely sensed data collected over El Reno, Oklahoma, during four days in June and July 1997, scale effects on the estimation of spatially distributed ET were investigated. Instantaneous estimates of latent and sensible heat flux were calculated using a two-source surface energy balance model driven by thermal infrared, visible-near infrared, and meteorological data. The heat flux estimates were verified by comparison to independent eddy-covariance observations. Outcomes of observations taken at coarser resolutions were simulated by aggregating remote sensor data and estimated surface energy balance components from the finest sensor resolution (12 meter) to hypothetical resolutions as coarse as one kilometer. Estimated surface energy flux components were found to be significantly dependent on observation scale. For example, average evaporative fraction varied from 0.79, using 12-m resolution data, to 0.93, using 1-km resolution data. Resolution effects upon flux estimates were related to a measure of landscape heterogeneity known as operational scale, reflecting the size of dominant landscape features. Energy flux estimates based on data at resolutions less than 100 m and much greater than 400 m showed a scale-dependent bias. But estimates derived from data taken at about 400-m resolution (the operational scale at El Reno) were susceptible to large error due to mixing of surface types. The El Reno experiments show that accurate instantaneous estimates of ET require precise image alignment and image resolutions finer than landscape operational scale. These findings are valuable for the design of sensors and experiments to quantify spatially-varying hydrologic processes.

Adaptive optics improves multiphoton super-resolution imaging

NASA Astrophysics Data System (ADS)

Zheng, Wei; Wu, Yicong; Winter, Peter; Shroff, Hari

2018-02-01

Three dimensional (3D) fluorescence microscopy has been essential for biological studies. It allows interrogation of structure and function at spatial scales spanning the macromolecular, cellular, and tissue levels. Critical factors to consider in 3D microscopy include spatial resolution, signal-to-noise (SNR), signal-to-background (SBR), and temporal resolution. Maintaining high quality imaging becomes progressively more difficult at increasing depth (where optical aberrations, induced by inhomogeneities of refractive index in the sample, degrade resolution and SNR), and in thick or densely labeled samples (where out-of-focus background can swamp the valuable, in-focus-signal from each plane). In this report, we introduce our new instrumentation to address these problems. A multiphoton structured illumination microscope was simply modified to integrate an adpative optics system for optical aberrations correction. Firstly, the optical aberrations are determined using direct wavefront sensing with a nonlinear guide star and subsequently corrected using a deformable mirror, restoring super-resolution information. We demonstrate the flexibility of our adaptive optics approach on a variety of semi-transparent samples, including bead phantoms, cultured cells in collagen gels and biological tissues. The performance of our super-resolution microscope is improved in all of these samples, as peak intensity is increased (up to 40-fold) and resolution recovered (up to 176+/-10 nm laterally and 729+/-39 nm axially) at depths up to 250 μm from the coverslip surface.

Spatial Heterodyne Observations of Water (SHOW) vapour in the upper troposphere and lower stratosphere from a high altitude aircraft: Modelling and sensitivity analysis

NASA Astrophysics Data System (ADS)

Langille, J. A.; Letros, D.; Zawada, D.; Bourassa, A.; Degenstein, D.; Solheim, B.

2018-04-01

A spatial heterodyne spectrometer (SHS) has been developed to measure the vertical distribution of water vapour in the upper troposphere and the lower stratosphere with a high vertical resolution (∼500 m). The Spatial Heterodyne Observations of Water (SHOW) instrument combines an imaging system with a monolithic field-widened SHS to observe limb scattered sunlight in a vibrational band of water (1363 nm-1366 nm). The instrument has been optimized for observations from NASA's ER-2 aircraft as a proof-of-concept for a future low earth orbit satellite deployment. A robust model has been developed to simulate SHOW ER-2 limb measurements and retrievals. This paper presents the simulation of the SHOW ER-2 limb measurements along a hypothetical flight track and examines the sensitivity of the measurement and retrieval approach. Water vapour fields from an Environment and Climate Change Canada forecast model are used to represent realistic spatial variability along the flight path. High spectral resolution limb scattered radiances are simulated using the SASKTRAN radiative transfer model. It is shown that the SHOW instrument onboard the ER-2 is capable of resolving the water vapour variability in the UTLS from approximately 12 km - 18 km with ±1 ppm accuracy. Vertical resolutions between 500 m and 1 km are feasible. The along track sampling capability of the instrument is also discussed.

Scales of snow depth variability in high elevation rangeland sagebrush

NASA Astrophysics Data System (ADS)

Tedesche, Molly E.; Fassnacht, Steven R.; Meiman, Paul J.

2017-09-01

In high elevation semi-arid rangelands, sagebrush and other shrubs can affect transport and deposition of wind-blown snow, enabling the formation of snowdrifts. Datasets from three field experiments were used to investigate the scales of spatial variability of snow depth around big mountain sagebrush ( Artemisia tridentata Nutt.) at a high elevation plateau rangeland in North Park, Colorado, during the winters of 2002, 2003, and 2008. Data were collected at multiple resolutions (0.05 to 25 m) and extents (2 to 1000 m). Finer scale data were collected specifically for this study to examine the correlation between snow depth, sagebrush microtopography, the ground surface, and the snow surface, as well as the temporal consistency of snow depth patterns. Variograms were used to identify the spatial structure and the Moran's I statistic was used to determine the spatial correlation. Results show some temporal consistency in snow depth at several scales. Plot scale snow depth variability is partly a function of the nature of individual shrubs, as there is some correlation between the spatial structure of snow depth and sagebrush, as well as between the ground and snow depth. The optimal sampling resolution appears to be 25-cm, but over a large area, this would require a multitude of samples, and thus a random stratified approach is recommended with a fine measurement resolution of 5-cm.

Using Imaging Methods to Interrogate Radiation-Induced Cell Signaling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shankaran, Harish; Weber, Thomas J.; Freiin von Neubeck, Claere H.

2012-04-01

There is increasing emphasis on the use of systems biology approaches to define radiation induced responses in cells and tissues. Such approaches frequently rely on global screening using various high throughput 'omics' platforms. Although these methods are ideal for obtaining an unbiased overview of cellular responses, they often cannot reflect the inherent heterogeneity of the system or provide detailed spatial information. Additionally, performing such studies with multiple sampling time points can be prohibitively expensive. Imaging provides a complementary method with high spatial and temporal resolution capable of following the dynamics of signaling processes. In this review, we utilize specific examplesmore » to illustrate how imaging approaches have furthered our understanding of radiation induced cellular signaling. Particular emphasis is placed on protein co-localization, and oscillatory and transient signaling dynamics.« less

a Spiral-Based Downscaling Method for Generating 30 M Time Series Image Data

NASA Astrophysics Data System (ADS)

Liu, B.; Chen, J.; Xing, H.; Wu, H.; Zhang, J.

2017-09-01

The spatial detail and updating frequency of land cover data are important factors influencing land surface dynamic monitoring applications in high spatial resolution scale. However, the fragmentized patches and seasonal variable of some land cover types (e. g. small crop field, wetland) make it labor-intensive and difficult in the generation of land cover data. Utilizing the high spatial resolution multi-temporal image data is a possible solution. Unfortunately, the spatial and temporal resolution of available remote sensing data like Landsat or MODIS datasets can hardly satisfy the minimum mapping unit and frequency of current land cover mapping / updating at the same time. The generation of high resolution time series may be a compromise to cover the shortage in land cover updating process. One of popular way is to downscale multi-temporal MODIS data with other high spatial resolution auxiliary data like Landsat. But the usual manner of downscaling pixel based on a window may lead to the underdetermined problem in heterogeneous area, result in the uncertainty of some high spatial resolution pixels. Therefore, the downscaled multi-temporal data can hardly reach high spatial resolution as Landsat data. A spiral based method was introduced to downscale low spatial and high temporal resolution image data to high spatial and high temporal resolution image data. By the way of searching the similar pixels around the adjacent region based on the spiral, the pixel set was made up in the adjacent region pixel by pixel. The underdetermined problem is prevented to a large extent from solving the linear system when adopting the pixel set constructed. With the help of ordinary least squares, the method inverted the endmember values of linear system. The high spatial resolution image was reconstructed on the basis of high spatial resolution class map and the endmember values band by band. Then, the high spatial resolution time series was formed with these high spatial resolution images image by image. Simulated experiment and remote sensing image downscaling experiment were conducted. In simulated experiment, the 30 meters class map dataset Globeland30 was adopted to investigate the effect on avoid the underdetermined problem in downscaling procedure and a comparison between spiral and window was conducted. Further, the MODIS NDVI and Landsat image data was adopted to generate the 30m time series NDVI in remote sensing image downscaling experiment. Simulated experiment results showed that the proposed method had a robust performance in downscaling pixel in heterogeneous region and indicated that it was superior to the traditional window-based methods. The high resolution time series generated may be a benefit to the mapping and updating of land cover data.

A straightforward approach for gated STED-FCS to investigate lipid membrane dynamics

PubMed Central

Clausen, Mathias P.; Sezgin, Erdinc; Bernardino de la Serna, Jorge; Waithe, Dominic; Lagerholm, B. Christoffer; Eggeling, Christian

2015-01-01

Recent years have seen the development of multiple technologies to investigate, with great spatial and temporal resolution, the dynamics of lipids in cellular and model membranes. One of these approaches is the combination of far-field super-resolution stimulated-emission-depletion (STED) microscopy with fluorescence correlation spectroscopy (FCS). STED-FCS combines the diffraction-unlimited spatial resolution of STED microscopy with the statistical accuracy of FCS to determine sub-millisecond-fast molecular dynamics with single-molecule sensitivity. A unique advantage of STED-FCS is that the observation spot for the FCS data recordings can be tuned to sub-diffraction scales, i.e. <200 nm in diameter, in a gradual manner to investigate fast diffusion of membrane-incorporated labelled entities. Unfortunately, so far the STED-FCS technology has mostly been applied on a few custom-built setups optimised for far-red fluorescent emitters. Here, we summarise the basics of the STED-FCS technology and highlight how it can give novel details into molecular diffusion modes. Most importantly, we present a straightforward way for performing STED-FCS measurements on an unmodified turnkey commercial system using a time-gated detection scheme. Further, we have evaluated the STED-FCS performance of different commonly used green emitting fluorescent dyes applying freely available, custom-written analysis software. PMID:26123184