Modeling Habitat Suitability of Migratory Birds from Remote Sensing Images Using Convolutional Neural Networks

PubMed Central

Su, Jin-He; Piao, Ying-Chao; Luo, Ze; Yan, Bao-Ping

2018-01-01

Simple Summary The understanding of the spatio-temporal distribution of the species habitats would facilitate wildlife resource management and conservation efforts. Existing methods have poor performance due to the limited availability of training samples. More recently, location-aware sensors have been widely used to track animal movements. The aim of the study was to generate suitability maps of bar-head geese using movement data coupled with environmental parameters, such as remote sensing images and temperature data. Therefore, we modified a deep convolutional neural network for the multi-scale inputs. The results indicate that the proposed method can identify the areas with the dense goose species around Qinghai Lake. In addition, this approach might also be interesting for implementation in other species with different niche factors or in areas where biological survey data are scarce. Abstract With the application of various data acquisition devices, a large number of animal movement data can be used to label presence data in remote sensing images and predict species distribution. In this paper, a two-stage classification approach for combining movement data and moderate-resolution remote sensing images was proposed. First, we introduced a new density-based clustering method to identify stopovers from migratory birds’ movement data and generated classification samples based on the clustering result. We split the remote sensing images into 16 × 16 patches and labeled them as positive samples if they have overlap with stopovers. Second, a multi-convolution neural network model is proposed for extracting the features from temperature data and remote sensing images, respectively. Then a Support Vector Machines (SVM) model was used to combine the features together and predict classification results eventually. The experimental analysis was carried out on public Landsat 5 TM images and a GPS dataset was collected on 29 birds over three years. The results indicated that our proposed method outperforms the existing baseline methods and was able to achieve good performance in habitat suitability prediction. PMID:29701686

Adaptive Sensing of Time Series with Application to Remote Exploration

NASA Technical Reports Server (NTRS)

Thompson, David R.; Cabrol, Nathalie A.; Furlong, Michael; Hardgrove, Craig; Low, Bryan K. H.; Moersch, Jeffrey; Wettergreen, David

2013-01-01

We address the problem of adaptive informationoptimal data collection in time series. Here a remote sensor or explorer agent throttles its sampling rate in order to track anomalous events while obeying constraints on time and power. This problem is challenging because the agent has limited visibility -- all collected datapoints lie in the past, but its resource allocation decisions require predicting far into the future. Our solution is to continually fit a Gaussian process model to the latest data and optimize the sampling plan on line to maximize information gain. We compare the performance characteristics of stationary and nonstationary Gaussian process models. We also describe an application based on geologic analysis during planetary rover exploration. Here adaptive sampling can improve coverage of localized anomalies and potentially benefit mission science yield of long autonomous traverses.

Model Checking the Remote Agent Planner

NASA Technical Reports Server (NTRS)

Khatib, Lina; Muscettola, Nicola; Havelund, Klaus; Norvig, Peter (Technical Monitor)

2001-01-01

This work tackles the problem of using Model Checking for the purpose of verifying the HSTS (Scheduling Testbed System) planning system. HSTS is the planner and scheduler of the remote agent autonomous control system deployed in Deep Space One (DS1). Model Checking allows for the verification of domain models as well as planning entries. We have chosen the real-time model checker UPPAAL for this work. We start by motivating our work in the introduction. Then we give a brief description of HSTS and UPPAAL. After that, we give a sketch for the mapping of HSTS models into UPPAAL and we present samples of plan model properties one may want to verify.

Reviews and Syntheses: optical sampling of the flux tower footprint

NASA Astrophysics Data System (ADS)

Gamon, J. A.

2015-07-01

The purpose of this review is to address the reasons and methods for conducting optical remote sensing within the flux tower footprint. Fundamental principles and conclusions gleaned from over 2 decades of proximal remote sensing at flux tower sites are reviewed. The organizing framework used here is the light-use efficiency (LUE) model, both because it is widely used, and because it provides a useful theoretical construct for integrating optical remote sensing with flux measurements. Multiple ways of driving this model, ranging from meteorological measurements to remote sensing, have emerged in recent years, making it a convenient conceptual framework for comparative experimental studies. New interpretations of established optical sampling methods, including the photochemical reflectance index (PRI) and solar-induced chlorophyll fluorescence (SIF), are discussed within the context of the LUE model. Multi-scale analysis across temporal and spatial axes is a central theme because such scaling can provide links between ecophysiological mechanisms detectable at the level of individual organisms and broad patterns emerging at larger scales, enabling evaluation of emergent properties and extrapolation to the flux footprint and beyond. Proper analysis of the sampling scale requires an awareness of sampling context that is often essential to the proper interpretation of optical signals. Additionally, the concept of optical types, vegetation exhibiting contrasting optical behavior in time and space, is explored as a way to frame our understanding of the controls on surface-atmosphere fluxes. Complementary normalized difference vegetation index (NDVI) and PRI patterns across ecosystems are offered as an example of this hypothesis, with the LUE model and light-response curve providing an integrating framework. I conclude that experimental approaches allowing systematic exploration of plant optical behavior in the context of the flux tower network provides a unique way to improve our understanding of environmental constraints and ecophysiological function. In addition to an enhanced mechanistic understanding of ecosystem processes, this integration of remote sensing with flux measurements offers many rich opportunities for upscaling, satellite validation, and informing practical management objectives ranging from assessing ecosystem health and productivity to quantifying biospheric carbon sequestration.

Interfacing geographic information systems and remote sensing for rural land-use analysis

NASA Technical Reports Server (NTRS)

Nellis, M. Duane; Lulla, Kamlesh; Jensen, John

1990-01-01

Recent advances in computer-based geographic information systems (GISs) are briefly reviewed, with an emphasis on the incorporation of remote-sensing data in GISs for rural applications. Topics addressed include sampling procedures for rural land-use analyses; GIS-based mapping of agricultural land use and productivity; remote sensing of land use and agricultural, forest, rangeland, and water resources; monitoring the dynamics of irrigation agriculture; GIS methods for detecting changes in land use over time; and the development of land-use modeling strategies.

Application of an imputation method for geospatial inventory of forest structural attributes across multiple spatial scales in the Lake States, U.S.A

NASA Astrophysics Data System (ADS)

Deo, Ram K.

Credible spatial information characterizing the structure and site quality of forests is critical to sustainable forest management and planning, especially given the increasing demands and threats to forest products and services. Forest managers and planners are required to evaluate forest conditions over a broad range of scales, contingent on operational or reporting requirements. Traditionally, forest inventory estimates are generated via a design-based approach that involves generalizing sample plot measurements to characterize an unknown population across a larger area of interest. However, field plot measurements are costly and as a consequence spatial coverage is limited. Remote sensing technologies have shown remarkable success in augmenting limited sample plot data to generate stand- and landscape-level spatial predictions of forest inventory attributes. Further enhancement of forest inventory approaches that couple field measurements with cutting edge remotely sensed and geospatial datasets are essential to sustainable forest management. We evaluated a novel Random Forest based k Nearest Neighbors (RF-kNN) imputation approach to couple remote sensing and geospatial data with field inventory collected by different sampling methods to generate forest inventory information across large spatial extents. The forest inventory data collected by the FIA program of US Forest Service was integrated with optical remote sensing and other geospatial datasets to produce biomass distribution maps for a part of the Lake States and species-specific site index maps for the entire Lake State. Targeting small-area application of the state-of-art remote sensing, LiDAR (light detection and ranging) data was integrated with the field data collected by an inexpensive method, called variable plot sampling, in the Ford Forest of Michigan Tech to derive standing volume map in a cost-effective way. The outputs of the RF-kNN imputation were compared with independent validation datasets and extant map products based on different sampling and modeling strategies. The RF-kNN modeling approach was found to be very effective, especially for large-area estimation, and produced results statistically equivalent to the field observations or the estimates derived from secondary data sources. The models are useful to resource managers for operational and strategic purposes.

Forestry applications project/timber resource. Sam Houston National forest inventory and development of a survey planning model

NASA Technical Reports Server (NTRS)

Colwell, R. N.

1976-01-01

The Forestry Applications Project has been directed towards solving the problem of meeting informational needs of the resource managers utilizing remote sensing data sources including satellite data, conventional aerial photography, and direct measurement on the ground in such combinations as needed to best achieve these goals. It is recognized that sampling plays an important role in generating relevant information for managing large geographic populations. The central problem, therefore, is to define the kind and amount of sampling and the place of remote sensing data sources in that sampling system to do the best possible job of meeting the manager's informational needs.

Remotely detected high-field MRI of porous samples

NASA Astrophysics Data System (ADS)

Seeley, Juliette A.; Han, Song-I.; Pines, Alexander

2004-04-01

Remote detection of NMR is a novel technique in which an NMR-active sensor surveys an environment of interest and retains memory of that environment to be recovered at a later time in a different location. The NMR or MRI information about the sensor nucleus is encoded and stored as spin polarization at the first location and subsequently moved to a different physical location for optimized detection. A dedicated probe incorporating two separate radio frequency (RF)—circuits was built for this purpose. The encoding solenoid coil was large enough to fit around the bulky sample matrix, while the smaller detection solenoid coil had not only a higher quality factor, but also an enhanced filling factor since the coil volume comprised purely the sensor nuclei. We obtained two-dimensional (2D) void space images of two model porous samples with resolution less than 1.4 mm 2. The remotely reconstructed images demonstrate the ability to determine fine structure with image quality superior to their directly detected counterparts and show the great potential of NMR remote detection for imaging applications that suffer from low sensitivity due to low concentrations and filling factor.

Improving inferences from fisheries capture-recapture studies through remote detection of PIT tags

USGS Publications Warehouse

Hewitt, David A.; Janney, Eric C.; Hayes, Brian S.; Shively, Rip S.

2010-01-01

Models for capture-recapture data are commonly used in analyses of the dynamics of fish and wildlife populations, especially for estimating vital parameters such as survival. Capture-recapture methods provide more reliable inferences than other methods commonly used in fisheries studies. However, for rare or elusive fish species, parameter estimation is often hampered by small probabilities of re-encountering tagged fish when encounters are obtained through traditional sampling methods. We present a case study that demonstrates how remote antennas for passive integrated transponder (PIT) tags can increase encounter probabilities and the precision of survival estimates from capture-recapture models. Between 1999 and 2007, trammel nets were used to capture and tag over 8,400 endangered adult Lost River suckers (Deltistes luxatus) during the spawning season in Upper Klamath Lake, Oregon. Despite intensive sampling at relatively discrete spawning areas, encounter probabilities from Cormack-Jolly-Seber models were consistently low (< 0.2) and the precision of apparent annual survival estimates was poor. Beginning in 2005, remote PIT tag antennas were deployed at known spawning locations to increase the probability of re-encountering tagged fish. We compare results based only on physical recaptures with results based on both physical recaptures and remote detections to demonstrate the substantial improvement in estimates of encounter probabilities (approaching 100%) and apparent annual survival provided by the remote detections. The richer encounter histories provided robust inferences about the dynamics of annual survival and have made it possible to explore more realistic models and hypotheses about factors affecting the conservation and recovery of this endangered species. Recent advances in technology related to PIT tags have paved the way for creative implementation of large-scale tagging studies in systems where they were previously considered impracticable.

Remote sensing new model for monitoring the east Asian migratory locust infections based on its breeding circle

NASA Astrophysics Data System (ADS)

Han, Xiuzhen; Ma, Jianwen; Bao, Yuhai

2006-12-01

Currently the function of operational locust monitor system mainly focused on after-hazards monitoring and assessment, and to found the way effectively to perform early warning and prediction has more practical meaning. Through 2001, 2002 two years continuously field sample and statistics for locusts eggs hatching, nymph growth, adults 3 phases observation, sample statistics and calculation, spectral measurements as well as synchronically remote sensing data processing we raise the view point of Remote Sensing three stage monitor the locust hazards. Based on the point of view we designed remote sensing monitor in three stages: (1) during the egg hitching phase remote sensing can retrieve parameters of land surface temperature (LST) and soil moisture; (2) during nymph growth phase locust increases appetite greatly and remote sensing can calculate vegetation index, leaf area index, vegetation cover and analysis changes; (3) during adult phase the locust move and assembly towards ponds and water ditches as well as less than 75% vegetation cover areas and remote sensing combination with field data can monitor and predicts potential areas for adult locusts to assembly. In this way the priority of remote sensing technology is elaborated effectively and it also provides technique support for the locust monitor system. The idea and techniques used in the study can also be used as reference for other plant diseases and insect pests.

Increasing precision of turbidity-based suspended sediment concentration and load estimates.

PubMed

Jastram, John D; Zipper, Carl E; Zelazny, Lucian W; Hyer, Kenneth E

2010-01-01

Turbidity is an effective tool for estimating and monitoring suspended sediments in aquatic systems. Turbidity can be measured in situ remotely and at fine temporal scales as a surrogate for suspended sediment concentration (SSC), providing opportunity for a more complete record of SSC than is possible with physical sampling approaches. However, there is variability in turbidity-based SSC estimates and in sediment loadings calculated from those estimates. This study investigated the potential to improve turbidity-based SSC, and by extension the resulting sediment loading estimates, by incorporating hydrologic variables that can be monitored remotely and continuously (typically 15-min intervals) into the SSC estimation procedure. On the Roanoke River in southwestern Virginia, hydrologic stage, turbidity, and other water-quality parameters were monitored with in situ instrumentation; suspended sediments were sampled manually during elevated turbidity events; samples were analyzed for SSC and physical properties including particle-size distribution and organic C content; and rainfall was quantified by geologic source area. The study identified physical properties of the suspended-sediment samples that contribute to SSC estimation variance and hydrologic variables that explained variability of those physical properties. Results indicated that the inclusion of any of the measured physical properties in turbidity-based SSC estimation models reduces unexplained variance. Further, the use of hydrologic variables to represent these physical properties, along with turbidity, resulted in a model, relying solely on data collected remotely and continuously, that estimated SSC with less variance than a conventional turbidity-based univariate model, allowing a more precise estimate of sediment loading, Modeling results are consistent with known mechanisms governing sediment transport in hydrologic systems.

Modeling the impacts of phenological and inter-annual changes in landscape metrics on local biodiversity of agricultural lands of Eastern Ontario using multi-spatial and multi-temporal remote sensing data

NASA Astrophysics Data System (ADS)

Alavi-Shoushtari, N.; King, D.

2017-12-01

Agricultural landscapes are highly variable ecosystems and are home to many local farmland species. Seasonal, phenological and inter-annual agricultural landscape dynamics have potential to affect the richness and abundance of farmland species. Remote sensing provides data and techniques which enable monitoring landscape changes in multiple temporal and spatial scales. MODIS high temporal resolution remote sensing images enable detection of seasonal and phenological trends, while Landsat higher spatial resolution images, with its long term archive enables inter-annual trend analysis over several decades. The objective of this study to use multi-spatial and multi-temporal remote sensing data to model the response of farmland species to landscape metrics. The study area is the predominantly agricultural region of eastern Ontario. 92 sample landscapes were selected within this region using a protocol designed to maximize variance in composition and configuration heterogeneity while controlling for amount of forest and spatial autocorrelation. Two sample landscape extents (1×1km and 3×3km) were selected to analyze the impacts of spatial scale on biodiversity response. Gamma diversity index data for four taxa groups (birds, butterflies, plants, and beetles) were collected during the summers of 2011 and 2012 within the cropped area of each landscape. To extract the seasonal and phenological metrics a 2000-2012 MODIS NDVI time-series was used, while a 1985-2012 Landsat time-series was used to model the inter-annual trends of change in the sample landscapes. The results of statistical modeling showed significant relationships between farmland biodiversity for several taxa and the phenological and inter-annual variables. The following general results were obtained: 1) Among the taxa groups, plant and beetles diversity was most significantly correlated with the phenological variables; 2) Those phenological variables which are associated with the variability in the start of season date across the sample landscapes and the variability in the corresponding NDVI values at that date showed the strongest correlation with the biodiversity indices; 3) The significance of the models improved when using 3×3km site extent both for MODIS and Landsat based models due most likely to the larger sample size over 3x3km.

Prediction of Chl-a concentrations in an eutrophic lake using ANN models with hybrid inputs

NASA Astrophysics Data System (ADS)

Aksoy, A.; Yuzugullu, O.

2017-12-01

Chlorophyll-a (Chl-a) concentrations in water bodies exhibit both spatial and temporal variations. As a result, frequent sampling is required with higher number of samples. This motivates the use of remote sensing as a monitoring tool. Yet, prediction performances of models that convert radiance values into Chl-a concentrations can be poor in shallow lakes. In this study, Chl-a concentrations in Lake Eymir, a shallow eutrophic lake in Ankara (Turkey), are determined using artificial neural network (ANN) models that use hybrid inputs composed of water quality and meteorological data as well as remotely sensed radiance values to improve prediction performance. Following a screening based on multi-collinearity and principal component analysis (PCA), dissolved-oxygen concentration (DO), pH, turbidity, and humidity were selected among several parameters as the constituents of the hybrid input dataset. Radiance values were obtained from QuickBird-2 satellite. Conversion of the hybrid input into Chl-a concentrations were studied for two different periods in the lake. ANN models were successful in predicting Chl-a concentrations. Yet, prediction performance declined for low Chl-a concentrations in the lake. In general, models with hybrid inputs were superior over the ones that solely used remotely sensed data.

Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

Treesearch

Ram Deo; Matthew Russell; Grant Domke; Hans-Erik Andersen; Warren Cohen; Christopher Woodall

2017-01-01

Large-area assessment of aboveground tree biomass (AGB) to inform regional or national forest monitoring programs can be efficiently carried out by combining remotely sensed data and field sample measurements through a generic statistical model, in contrast to site-specific models. We integrated forest inventory plot data with spatial predictors from Landsat time-...

Adaptive Sampling of Time Series During Remote Exploration

NASA Technical Reports Server (NTRS)

Thompson, David R.

2012-01-01

This work deals with the challenge of online adaptive data collection in a time series. A remote sensor or explorer agent adapts its rate of data collection in order to track anomalous events while obeying constraints on time and power. This problem is challenging because the agent has limited visibility (all its datapoints lie in the past) and limited control (it can only decide when to collect its next datapoint). This problem is treated from an information-theoretic perspective, fitting a probabilistic model to collected data and optimizing the future sampling strategy to maximize information gain. The performance characteristics of stationary and nonstationary Gaussian process models are compared. Self-throttling sensors could benefit environmental sensor networks and monitoring as well as robotic exploration. Explorer agents can improve performance by adjusting their data collection rate, preserving scarce power or bandwidth resources during uninteresting times while fully covering anomalous events of interest. For example, a remote earthquake sensor could conserve power by limiting its measurements during normal conditions and increasing its cadence during rare earthquake events. A similar capability could improve sensor platforms traversing a fixed trajectory, such as an exploration rover transect or a deep space flyby. These agents can adapt observation times to improve sample coverage during moments of rapid change. An adaptive sampling approach couples sensor autonomy, instrument interpretation, and sampling. The challenge is addressed as an active learning problem, which already has extensive theoretical treatment in the statistics and machine learning literature. A statistical Gaussian process (GP) model is employed to guide sample decisions that maximize information gain. Nonsta tion - ary (e.g., time-varying) covariance relationships permit the system to represent and track local anomalies, in contrast with current GP approaches. Most common GP models are stationary, e.g., the covariance relationships are time-invariant. In such cases, information gain is independent of previously collected data, and the optimal solution can always be computed in advance. Information-optimal sampling of a stationary GP time series thus reduces to even spacing, and such models are not appropriate for tracking localized anomalies. Additionally, GP model inference can be computationally expensive.

Object-based vegetation classification with high resolution remote sensing imagery

NASA Astrophysics Data System (ADS)

Yu, Qian

Vegetation species are valuable indicators to understand the earth system. Information from mapping of vegetation species and community distribution at large scales provides important insight for studying the phenological (growth) cycles of vegetation and plant physiology. Such information plays an important role in land process modeling including climate, ecosystem and hydrological models. The rapidly growing remote sensing technology has increased its potential in vegetation species mapping. However, extracting information at a species level is still a challenging research topic. I proposed an effective method for extracting vegetation species distribution from remotely sensed data and investigated some ways for accuracy improvement. The study consists of three phases. Firstly, a statistical analysis was conducted to explore the spatial variation and class separability of vegetation as a function of image scale. This analysis aimed to confirm that high resolution imagery contains the information on spatial vegetation variation and these species classes can be potentially separable. The second phase was a major effort in advancing classification by proposing a method for extracting vegetation species from high spatial resolution remote sensing data. The proposed classification employs an object-based approach that integrates GIS and remote sensing data and explores the usefulness of ancillary information. The whole process includes image segmentation, feature generation and selection, and nearest neighbor classification. The third phase introduces a spatial regression model for evaluating the mapping quality from the above vegetation classification results. The effects of six categories of sample characteristics on the classification uncertainty are examined: topography, sample membership, sample density, spatial composition characteristics, training reliability and sample object features. This evaluation analysis answered several interesting scientific questions such as (1) whether the sample characteristics affect the classification accuracy and how significant if it does; (2) how much variance of classification uncertainty can be explained by above factors. This research is carried out on a hilly peninsular area in Mediterranean climate, Point Reyes National Seashore (PRNS) in Northern California. The area mainly consists of a heterogeneous, semi-natural broadleaf and conifer woodland, shrub land, and annual grassland. A detailed list of vegetation alliances is used in this study. Research results from the first phase indicates that vegetation spatial variation as reflected by the average local variance (ALV) keeps a high level of magnitude between 1 m and 4 m resolution. (Abstract shortened by UMI.)

Knowledge-guided golf course detection using a convolutional neural network fine-tuned on temporally augmented data

NASA Astrophysics Data System (ADS)

Chen, Jingbo; Wang, Chengyi; Yue, Anzhi; Chen, Jiansheng; He, Dongxu; Zhang, Xiuyan

2017-10-01

The tremendous success of deep learning models such as convolutional neural networks (CNNs) in computer vision provides a method for similar problems in the field of remote sensing. Although research on repurposing pretrained CNN to remote sensing tasks is emerging, the scarcity of labeled samples and the complexity of remote sensing imagery still pose challenges. We developed a knowledge-guided golf course detection approach using a CNN fine-tuned on temporally augmented data. The proposed approach is a combination of knowledge-driven region proposal, data-driven detection based on CNN, and knowledge-driven postprocessing. To confront data complexity, knowledge-derived cooccurrence, composition, and area-based rules are applied sequentially to propose candidate golf regions. To confront sample scarcity, we employed data augmentation in the temporal domain, which extracts samples from multitemporal images. The augmented samples were then used to fine-tune a pretrained CNN for golf detection. Finally, commission error was further suppressed by postprocessing. Experiments conducted on GF-1 imagery prove the effectiveness of the proposed approach.

Coastal water optical properties from four Southeast Asian coastal environments ranging from relatively pristine to heavily impacted

NASA Astrophysics Data System (ADS)

Osburn, C. L.; Boyd, T. J.; Anastasiou, C. J.; Thao, P. T. P.; Reid, J. S.

2016-02-01

Optical measurements (absorbance, EEM fluorescence, remote sensing reflectance) and concurrently-collected sensor-based data (CDOM, chlorophyll-a, salinity, turbidity, and temperature) were used to link optical properties to water mass characteristics. Data and samples were collected during four field events in the Philippines (SEP2011, SEP2012 - transects from Manila to Palawan Island), Thailand (MAR2012 - Pattaya Beach area) and Vietnam (MAR2012 - Nha Trang and Ha Long Bay). EEM fluorescence spectra from each site were modeled using PARAFAC to identify representative fluorophores. Remote sensing reflectance was modeled using PCA, determining spectral loadings showing variation in samples from each site. These synthesized model data and sensor-based measurements were collated and ordinated using PCA to determine if optical properties could be linked to water quality and biogeochemical measures. PCA models at each site showed stations nearest to the coastline falling near or outside 95% confidence regions. Initial results indicate protein-like fluorophores were found in lower salinity waters and more heavily-impacted regions (Manila Bay - Philippines, Nha Trang River - Vietnam, Bang Pakong River - Thailand). Spectral slope and an component loading from remote sensing reflectance appeared to co-vary with sensor-derived CDOM fluorescence. Results from intra- and inter-site comparisons and linkages to biogeochemical parameters will be presented.

Remote water monitoring system

NASA Technical Reports Server (NTRS)

Grana, D. C.; Haynes, D. P. (Inventor)

1978-01-01

A remote water monitoring system is described that integrates the functions of sampling, sample preservation, sample analysis, data transmission and remote operation. The system employs a floating buoy carrying an antenna connected by lines to one or more sampling units containing several sample chambers. Receipt of a command signal actuates a solenoid to open an intake valve outward from the sampling unit and communicates the water sample to an identifiable sample chamber. Such response to each signal receipt is repeated until all sample chambers are filled in a sample unit. Each sample taken is analyzed by an electrochemical sensor for a specific property and the data obtained is transmitted to a remote sending and receiving station. Thereafter, the samples remain isolated in the sample chambers until the sampling unit is recovered and the samples removed for further laboratory analysis.

Use of remote sensing and a geographical information system in a national helminth control programme in Chad.

PubMed Central

Brooker, Simon; Beasley, Michael; Ndinaromtan, Montanan; Madjiouroum, Ester Mobele; Baboguel, Marie; Djenguinabe, Elie; Hay, Simon I.; Bundy, Don A. P.

2002-01-01

OBJECTIVE: To design and implement a rapid and valid epidemiological assessment of helminths among schoolchildren in Chad using ecological zones defined by remote sensing satellite sensor data and to investigate the environmental limits of helminth distribution. METHODS: Remote sensing proxy environmental data were used to define seven ecological zones in Chad. These were combined with population data in a geographical information system (GIS) in order to define a sampling protocol. On this basis, 20 schools were surveyed. Multilevel analysis, by means of generalized estimating equations to account for clustering at the school level, was used to investigate the relationship between infection patterns and key environmental variables. FINDINGS: In a sample of 1023 schoolchildren, 22.5% were infected with Schistosoma haematobium and 32.7% with hookworm. None were infected with Ascaris lumbricoides or Trichuris trichiura. The prevalence of S. haematobium and hookworm showed marked geographical heterogeneity and the observed patterns showed a close association with the defined ecological zones and significant relationships with environmental variables. These results contribute towards defining the thermal limits of geohelminth species. Predictions of infection prevalence were made for each school surveyed with the aid of models previously developed for Cameroon. These models correctly predicted that A. lumbricoides and T. trichiura would not occur in Chad but the predictions for S. haematobium were less reliable at the school level. CONCLUSION: GIS and remote sensing can play an important part in the rapid planning of helminth control programmes where little information on disease burden is available. Remote sensing prediction models can indicate patterns of geohelminth infection but can only identify potential areas of high risk for S. haematobium. PMID:12471398

Environmental analysis using integrated GIS and remotely sensed data - Some research needs and priorities

NASA Technical Reports Server (NTRS)

Davis, Frank W.; Quattrochi, Dale A.; Ridd, Merrill K.; Lam, Nina S.-N.; Walsh, Stephen J.

1991-01-01

This paper discusses some basic scientific issues and research needs in the joint processing of remotely sensed and GIS data for environmental analysis. Two general topics are treated in detail: (1) scale dependence of geographic data and the analysis of multiscale remotely sensed and GIS data, and (2) data transformations and information flow during data processing. The discussion of scale dependence focuses on the theory and applications of spatial autocorrelation, geostatistics, and fractals for characterizing and modeling spatial variation. Data transformations during processing are described within the larger framework of geographical analysis, encompassing sampling, cartography, remote sensing, and GIS. Development of better user interfaces between image processing, GIS, database management, and statistical software is needed to expedite research on these and other impediments to integrated analysis of remotely sensed and GIS data.

Proceedings of the eighth annual forest inventory and analysis symposium

Treesearch

Ronald E. McRoberts; Gregory A. Reams; Paul C. Van Deusen; William H., eds. McWilliams

2009-01-01

Documents contributions to forest inventory in the areas of sampling, remote sensing, modeling, information management and analysis for the Forest Inventory and Analysis program of the Forest Service.

An ecological assessment of pasturelands in the Balkhash area of Kazakhstan with remote sensing and models

NASA Astrophysics Data System (ADS)

Lebed, L.; Qi, J.; Heilman, P.

2012-06-01

The 187 million hectares of pasturelands in Kazakhstan play a key role in the nation’s economy, as livestock production accounted for 54% of total agricultural production in 2010. However, more than half of these lands have been degraded as a result of unregulated grazing practices. Therefore, effective long term ecological monitoring of pasturelands in Kazakhstan is imperative to ensure sustainable pastureland management. As a case study in this research, we demonstrated how the ecological conditions could be assessed with remote sensing technologies and pastureland models. The example focuses on the southern Balkhash area with study sites on a foothill plain with Artemisia-ephemeral plants and a sandy plain with psammophilic vegetation in the Turan Desert. The assessment was based on remotely sensed imagery and meteorological data, a geobotanical archive and periodic ground sampling. The Pasture agrometeorological model was used to calculate biological, ecological and economic indicators to assess pastureland condition. The results showed that field surveys, meteorological observations, remote sensing and ecological models, such as Pasture, could be combined to effectively assess the ecological conditions of pasturelands and provide information about forage production that is critically important for balancing grazing and ecological conservation.

Hybrid Optimal Design of the Eco-Hydrological Wireless Sensor Network in the Middle Reach of the Heihe River Basin, China

PubMed Central

Kang, Jian; Li, Xin; Jin, Rui; Ge, Yong; Wang, Jinfeng; Wang, Jianghao

2014-01-01

The eco-hydrological wireless sensor network (EHWSN) in the middle reaches of the Heihe River Basin in China is designed to capture the spatial and temporal variability and to estimate the ground truth for validating the remote sensing productions. However, there is no available prior information about a target variable. To meet both requirements, a hybrid model-based sampling method without any spatial autocorrelation assumptions is developed to optimize the distribution of EHWSN nodes based on geostatistics. This hybrid model incorporates two sub-criteria: one for the variogram modeling to represent the variability, another for improving the spatial prediction to evaluate remote sensing productions. The reasonability of the optimized EHWSN is validated from representativeness, the variogram modeling and the spatial accuracy through using 15 types of simulation fields generated with the unconditional geostatistical stochastic simulation. The sampling design shows good representativeness; variograms estimated by samples have less than 3% mean error relative to true variograms. Then, fields at multiple scales are predicted. As the scale increases, estimated fields have higher similarities to simulation fields at block sizes exceeding 240 m. The validations prove that this hybrid sampling method is effective for both objectives when we do not know the characteristics of an optimized variables. PMID:25317762

Hybrid optimal design of the eco-hydrological wireless sensor network in the middle reach of the Heihe River Basin, China.

PubMed

Kang, Jian; Li, Xin; Jin, Rui; Ge, Yong; Wang, Jinfeng; Wang, Jianghao

2014-10-14

The eco-hydrological wireless sensor network (EHWSN) in the middle reaches of the Heihe River Basin in China is designed to capture the spatial and temporal variability and to estimate the ground truth for validating the remote sensing productions. However, there is no available prior information about a target variable. To meet both requirements, a hybrid model-based sampling method without any spatial autocorrelation assumptions is developed to optimize the distribution of EHWSN nodes based on geostatistics. This hybrid model incorporates two sub-criteria: one for the variogram modeling to represent the variability, another for improving the spatial prediction to evaluate remote sensing productions. The reasonability of the optimized EHWSN is validated from representativeness, the variogram modeling and the spatial accuracy through using 15 types of simulation fields generated with the unconditional geostatistical stochastic simulation. The sampling design shows good representativeness; variograms estimated by samples have less than 3% mean error relative to true variograms. Then, fields at multiple scales are predicted. As the scale increases, estimated fields have higher similarities to simulation fields at block sizes exceeding 240 m. The validations prove that this hybrid sampling method is effective for both objectives when we do not know the characteristics of an optimized variables.

Evaluation of Physiologically-Based Artificial Neural Network Models to Detect Operator Workload in Remotely Piloted Aircraft Operations

DTIC Science & Technology

2016-07-13

to a computer via Bluetooth . Respiration is captured as a breathing waveform signal using a capacitive pressure sensor, sampled at 18 Hz. The...dropouts in the Bluetooth signal and artifacts caused by body movement. Workload models. Four artificial neural network models were created using

Martian Radiative Transfer Modeling Using the Optimal Spectral Sampling Method

NASA Technical Reports Server (NTRS)

Eluszkiewicz, J.; Cady-Pereira, K.; Uymin, G.; Moncet, J.-L.

2005-01-01

The large volume of existing and planned infrared observations of Mars have prompted the development of a new martian radiative transfer model that could be used in the retrievals of atmospheric and surface properties. The model is based on the Optimal Spectral Sampling (OSS) method [1]. The method is a fast and accurate monochromatic technique applicable to a wide range of remote sensing platforms (from microwave to UV) and was originally developed for the real-time processing of infrared and microwave data acquired by instruments aboard the satellites forming part of the next-generation global weather satellite system NPOESS (National Polarorbiting Operational Satellite System) [2]. As part of our on-going research related to the radiative properties of the martian polar caps, we have begun the development of a martian OSS model with the goal of using it to perform self-consistent atmospheric corrections necessary to retrieve caps emissivity from the Thermal Emission Spectrometer (TES) spectra. While the caps will provide the initial focus area for applying the new model, it is hoped that the model will be of interest to the wider Mars remote sensing community.

Proceedings of the fourth annual Forest Inventory and Analysis symposium

Treesearch

Ronald E. McRoberts; Gregory A Reams; Paul C. Van Deusen; William H. McWilliams; Chris J. Cieszewski; Chris J., eds. Cieszewski

2005-01-01

Documents contributions to forest inventory in the areas of sampling, remote sensing, modeling, information management and analysis for the Forest Inventory and Analysis program of the USDA Forest Service.

Proceedings of the seventh annual forest inventory and analysis symposium

Treesearch

Ronald E. McRoberts; Gregory A. Reams; Paul C. Van Deusen; William H., eds. McWilliams

2007-01-01

Documents contributions to forest inventory in the areas of sampling, remote sensing, modeling, information management and analysis for the Forest Inventory and Analysis program of the USDA Forest Service.

Proceedings of the sixth annual forest inventory and analysis symposium

Treesearch

Ronald E. McRoberts; Gregory A. Reams; Paul C. Van Duesen; William H., eds. McWilliams

2006-01-01

Documents contributions to forest inventory in the area of sampling, remote sensing, modeling, information management, and analysis for the Forest Inventory and Analysis program of the USDA Forest Service.

Geographical and Vertical Distribution of Organic Aerosol (OA) during ATom-1 and 2: Chemical removal and aging as a function of photochemical age

NASA Astrophysics Data System (ADS)

Campuzano Jost, P.; Schroder, J. C.; Nault, B.; Day, D. A.; Jimenez, J. L.; Heald, C. L.; Hodzic, A.; Katich, J. M.; Schwarz, J. P.; Blake, N. J.; Blake, D. R.; Daube, B. C.; Wofsy, S. C.; Ray, E. A.; Bian, H.; Colarco, P. R.; Chin, M.; Pawson, S.; Newman, P. A.

2017-12-01

Submicron aerosols in the remote free troposphere (FT) originate mostly from long-range transport from distant biogenic, anthropogenic, and biomass burning sources. Very limited local production in this region heightens the sensitivity of aerosol concentrations to slow removal processes. As yet, few studies with an advanced aerosol payload have targeted the remote FT. Current global models exhibit a very large diversity in predicting aerosol concentrations in these regions of the atmosphere, particularly when trying to model organic aerosol (OA), which, together with sulfate, is the most prevalent type of non-refractory aerosol in the remote FT. As part of NASA's Atmospheric Tomography (ATom) aircraft mission, we have acquired a global dataset of organic aerosol (OA) concentration and composition over the remote Atlantic and Pacific Oceans from 0 to 12 km and from 65 S to 80 N for both Summer and Winter seasons. This dataset provides unique new constraints on the spatial distribution of OA and its contribution to the global aerosol background; of particular interest are the OA/Sulfate ratio and OA oxidation state that are critical for estimating the activity of cloud condensation nuclei (CCN) in the remote troposphere. We find that, except for the cleanest of the ATom-sampled airmasses, OA concentrations are comparable and often larger than sulfate. OA was highly oxidized, significantly more than over the continental FT, with O:C ratios often in excess of 1 (i.e. OA/OC >2.5). Using several different hydrocarbon-ratio based photochemical clocks in combination with backtrajectories to infer the age of the airmasses sampled during ATom, we estimate that the lifetime of OA in the remote FT is on the order of 10 days. This is significantly shorter than the FT lifetime assuming just wet and dry deposition as the primary loss mechanisms, and suggests a chemical removal mechanism such as heterogeneous oxidation or photolysis. This provides a key constraint for modeling of OA in the FT, based solely on measurements. The likelihood of different chemical removal mechanisms will be discussed and their potential implementation in global models such as GEOS-Chem explored. The contributions of methanesulfonic acid (MSA) and particulate organic nitrates (pRONO2) to total OA in the remote troposphere will be discussed as well.

Mapping CDOM Concentration in Waters Influenced by the Mississippi River Plume

NASA Technical Reports Server (NTRS)

Miller, Richard L.; DelCastillo, Carlos E.; Powell, Rodney T.; DSa, Eurico; Spiering, Bruce

2002-01-01

Colored dissolved organic matter (CDOM) is often an important component of the organic carbon pool in river-dominated coastal margins. CDOM directly influences remote sensing applications through its strong absorption in the UV and blue regions of the spectrum. This effect can complicate the use of chlorophyll a retrieval algorithms and phytoplankton production models that are based on remotely sensed ocean color. As freshwater input is the principle source of CDOM in coastal margins, CDOM distribution can often be described by conservative mixing with open ocean waters and may serve as an optical tracer of riverine water. Hence, there is considerable interest in the ability to accurately measure and map CDOM concentrations as well as understand the processes that govern the optical properties and distribution of CDOM in coastal environments. We are examining CDOM dynamics in the waters influenced by the Mississippi River plume. Our program incorporates discrete samples, flow-through measurements, and remote sensing. CDOM absorption spectra of discrete samples are measured at sea using a portable, multiple pathlength waveguide system. A SAFire multi-spectral fluorescence meter provides spectral characterization of CDOM (fluorescence and absorption) using a ship flow-through system for continuous surface mapping. In situ reflectance spectra are obtained by a hand held spectroradiometer. Remotely sensed images are obtained from the SeaWiFS and CRIS (Coastal Research Imaging Spectrometer) instruments. We describe here the instruments used, sampling protocols employed, and the relationships derived between in situ measurements and remotely sensed data for this optically complex environment.

Sampling design for an integrated socioeconomic and ecological survey by using satellite remote sensing and ordination

PubMed Central

Binford, Michael W.; Lee, Tae Jeong; Townsend, Robert M.

2004-01-01

Environmental variability is an important risk factor in rural agricultural communities. Testing models requires empirical sampling that generates data that are representative in both economic and ecological domains. Detrended correspondence analysis of satellite remote sensing data were used to design an effective low-cost sampling protocol for a field study to create an integrated socioeconomic and ecological database when no prior information on ecology of the survey area existed. We stratified the sample for the selection of tambons from various preselected provinces in Thailand based on factor analysis of spectral land-cover classes derived from satellite data. We conducted the survey for the sampled villages in the chosen tambons. The resulting data capture interesting variations in soil productivity and in the timing of good and bad years, which a purely random sample would likely have missed. Thus, this database will allow tests of hypotheses concerning the effect of credit on productivity, the sharing of idiosyncratic risks, and the economic influence of environmental variability. PMID:15254298

Pairing field methods to improve inference in wildlife surveys while accommodating detection covariance

USGS Publications Warehouse

Clare, John; McKinney, Shawn T.; DePue, John E.; Loftin, Cynthia S.

2017-01-01

It is common to use multiple field sampling methods when implementing wildlife surveys to compare method efficacy or cost efficiency, integrate distinct pieces of information provided by separate methods, or evaluate method-specific biases and misclassification error. Existing models that combine information from multiple field methods or sampling devices permit rigorous comparison of method-specific detection parameters, enable estimation of additional parameters such as false-positive detection probability, and improve occurrence or abundance estimates, but with the assumption that the separate sampling methods produce detections independently of one another. This assumption is tenuous if methods are paired or deployed in close proximity simultaneously, a common practice that reduces the additional effort required to implement multiple methods and reduces the risk that differences between method-specific detection parameters are confounded by other environmental factors. We develop occupancy and spatial capture–recapture models that permit covariance between the detections produced by different methods, use simulation to compare estimator performance of the new models to models assuming independence, and provide an empirical application based on American marten (Martes americana) surveys using paired remote cameras, hair catches, and snow tracking. Simulation results indicate existing models that assume that methods independently detect organisms produce biased parameter estimates and substantially understate estimate uncertainty when this assumption is violated, while our reformulated models are robust to either methodological independence or covariance. Empirical results suggested that remote cameras and snow tracking had comparable probability of detecting present martens, but that snow tracking also produced false-positive marten detections that could potentially substantially bias distribution estimates if not corrected for. Remote cameras detected marten individuals more readily than passive hair catches. Inability to photographically distinguish individual sex did not appear to induce negative bias in camera density estimates; instead, hair catches appeared to produce detection competition between individuals that may have been a source of negative bias. Our model reformulations broaden the range of circumstances in which analyses incorporating multiple sources of information can be robustly used, and our empirical results demonstrate that using multiple field-methods can enhance inferences regarding ecological parameters of interest and improve understanding of how reliably survey methods sample these parameters.

The NASA CYGNSS mission: a pathfinder for GNSS scatterometry remote sensing applications

NASA Astrophysics Data System (ADS)

Rose, Randy; Gleason, Scott; Ruf, Chris

2014-10-01

Global Navigation Satellite System (GNSS) based scatterometry offers breakthrough opportunities for wave, wind, ice, and soil moisture remote sensing. Recent developments in electronics and nano-satellite technologies combined with modeling techniques developed over the past 20 years are enabling a new class of remote sensing capabilities that present more cost effective solutions to existing problems while opening new applications of Earth remote sensing. Key information about the ocean and global climate is hidden from existing space borne observatories because of the frequency band in which they operate. Using GNSS-based bi-static scatterometry performed by a constellation of microsatellites offers remote sensing of ocean wave, wind, and ice data with unprecedented temporal resolution and spatial coverage across the full dynamic range of ocean wind speeds in all precipitating conditions. The NASA Cyclone Global Navigation Satellite System (CYGNSS) is a space borne mission being developed to study tropical cyclone inner core processes. CYGNSS consists of 8 GPS bi-static radar receivers to be deployed on separate micro-satellites in October 2016. CYGNSS will provide data to address what are thought to be the principle deficiencies with current tropical cyclone intensity forecasts: inadequate observations and modeling of the inner core. The inadequacy in observations results from two causes: 1) Much of the inner core ocean surface is obscured from conventional remote sensing instruments by intense precipitation in the eye wall and inner rain bands. 2) The rapidly evolving (genesis and intensification) stages of the tropical cyclone life cycle are poorly sampled in time by conventional polar-orbiting, wide-swath surface wind imagers. It is anticipated that numerous additional Earth science applications can also benefit from the cost effective high spatial and temporal sampling capabilities of GNSS remote sensing. These applications include monitoring of rough and dangerous sea states, global observations of sea ice cover and extent, meso-scale ocean circulation studies, and near surface soil moisture observations. This presentation provides a primer for GNSS based scatterometry, an overview of NASA's CYGNSS mission and its expected performance, as well as a summary of possible other GNSS based remote sensing applications.

a Novel Deep Convolutional Neural Network for Spectral-Spatial Classification of Hyperspectral Data

NASA Astrophysics Data System (ADS)

Li, N.; Wang, C.; Zhao, H.; Gong, X.; Wang, D.

2018-04-01

Spatial and spectral information are obtained simultaneously by hyperspectral remote sensing. Joint extraction of these information of hyperspectral image is one of most import methods for hyperspectral image classification. In this paper, a novel deep convolutional neural network (CNN) is proposed, which extracts spectral-spatial information of hyperspectral images correctly. The proposed model not only learns sufficient knowledge from the limited number of samples, but also has powerful generalization ability. The proposed framework based on three-dimensional convolution can extract spectral-spatial features of labeled samples effectively. Though CNN has shown its robustness to distortion, it cannot extract features of different scales through the traditional pooling layer that only have one size of pooling window. Hence, spatial pyramid pooling (SPP) is introduced into three-dimensional local convolutional filters for hyperspectral classification. Experimental results with a widely used hyperspectral remote sensing dataset show that the proposed model provides competitive performance.

Detection of Endolithes Using Infrared Spectroscopy

NASA Astrophysics Data System (ADS)

Dumas, S.; Dutil, Y.; Joncas, G.

2009-12-01

On Earth, the Dry Valleys of Antarctica provide the closest martian-like environment for the study of extremophiles. Colonies of bacterias are protected from the freezing temperatures, the drought and UV light. They represent almost half of the biomass of those regions. Due to their resilience, endolithes are one possible model of martian biota. We propose to use infrared spectroscopy to remotely detect those colonies even if there is no obvious sign of their presence. This remote sensing approach reduces the risk of contamination or damage to the samples.

Hyperspectral Remote Sensing of the Coastal Ocean: Adaptive Sampling and Forecasting of In situ Optical Properties

DTIC Science & Technology

2003-09-30

We are developing an integrated rapid environmental assessment capability that will be used to feed an ocean nowcast/forecast system. The goal is to develop a capacity for predicting the dynamics in inherent optical properties in coastal waters. This is being accomplished by developing an integrated observation system that is being coupled to a data assimilative hydrodynamic bio-optical ecosystem model. The system was used adaptively to calibrate hyperspectral remote sensing sensors in optically complex nearshore coastal waters.

Developing the remote sensing-based water environmental model for monitoring alpine river water environment over Plateau cold zone

NASA Astrophysics Data System (ADS)

You, Y.; Wang, S.; Yang, Q.; Shen, M.; Chen, G.

2017-12-01

Alpine river water environment on the Plateau (such as Tibetan Plateau, China) is a key indicator for water security and environmental security in China. Due to the complex terrain and various surface eco-environment, it is a very difficult to monitor the water environment over the complex land surface of the plateau. The increasing availability of remote sensing techniques with appropriate spatiotemporal resolutions, broad coverage and low costs allows for effective monitoring river water environment on the Plateau, particularly in remote and inaccessible areas where are lack of in situ observations. In this study, we propose a remote sense-based monitoring model by using multi-platform remote sensing data for monitoring alpine river environment. In this study some parameterization methodologies based on satellite remote sensing data and field observations have been proposed for monitoring the water environmental parameters (including chlorophyll-a concentration (Chl-a), water turbidity (WT) or water clarity (SD), total nitrogen (TN), total phosphorus (TP), and total organic carbon (TOC)) over the china's southwest highland rivers, such as the Brahmaputra. First, because most sensors do not collect multiple observations of a target in a single pass, data from multiple orbits or acquisition times may be used, and varying atmospheric and irradiance effects must be reconciled. So based on various types of satellite data, at first we developed the techniques of multi-sensor data correction, atmospheric correction. Second, we also built the inversion spectral database derived from long-term remote sensing data and field sampling data. Then we have studied and developed a high-precision inversion model over the southwest highland river backed by inversion spectral database through using the techniques of multi-sensor remote sensing information optimization and collaboration. Third, take the middle reaches of the Brahmaputra river as the study area, we validated the key water environmental parameters and further improved the inversion model. The results indicate that our proposed water environment inversion model can be a good inversion for alpine water environmental parameters, and can improve the monitoring and warning ability for the alpine river water environment in the future.

[Prediction models of soil organic matter based on spectral curve in the upstream of Heihe basin].

PubMed

Liu, Jiao; Li, Yi; Liu, Shi-Bin

2013-12-01

Benefiting from the high spectral resolution, ground hyperspectral remote sensing technology can express the ground surface feature in detail, meanwhile, multispectral remote sensing has more advantages in studying the features in a large space time region, because of its long time-series images and wide coverage. Investigating the prediction models between the soil organic matter (SOM) content and the hyperspectral data and the sensitive bands based on different indices mathematically obtained from reflectance could combine the advantages of both kinds of spectral data, and provide a new method to search the spatio-temporal characteristics of SOM. Two hundred twenty three soil samples were chosen from the upper reaches of Heihe Basin to measure the SOM content and hyperspectral curve. Taking 181 of them, the stepwise linear regression methods were used to establish models between the SOM and five indices, including reflectance (lambda), reciprocal (REC), logarithm of the reciprocal (LR), continuum-removal (CR) and the first derivative reflectance (FDR). After then, the left 42 samples were used for model validation: firstly, the best model of the same index was chosen by the values of Pearson correlation coefficient (r) and Root mean squared error (RMSE) between the measured value and predicted value; secondly, the best models of different indices were compared. As a result, the model built by reflectance has a better estimation of SOM with the r: 0.863 and RMSE: 4.79. And the sensitive bands of the reflectance model contain 474 nm during TM1, 636 nm during TM3 and 1 632 nm during TM5. This result could be a reference for the retrieval of SOM content of the upper reaches by using the TM remote sensing data.

[Hyperspectral remote sensing in monitoring the vegetation heavy metal pollution].

PubMed

Li, Na; Lü, Jian-sheng; Altemann, W

2010-09-01

Mine exploitation aggravates the environment pollution. The large amount of heavy metal element in the drainage of slag from the mine pollutes the soil seriously, doing harm to the vegetation growing and human health. The investigation of mining environment pollution is urgent, in which remote sensing, as a new technique, helps a lot. In the present paper, copper mine in Dexing was selected as the study area and China sumac as the study plant. Samples and spectral data in field were gathered and analyzed in lab. The regression model from spectral characteristics for heavy metal content was built, and the feasibility of hyperspectral remote sensing in environment pollution monitoring was testified.

Estimating wildfire risk on a Mojave Desert landscape using remote sensing and field sampling

USGS Publications Warehouse

Van Linn, Peter F.; Nussear, Kenneth E.; Esque, Todd C.; DeFalco, Lesley A.; Inman, Richard D.; Abella, Scott R.

2013-01-01

Predicting wildfires that affect broad landscapes is important for allocating suppression resources and guiding land management. Wildfire prediction in the south-western United States is of specific concern because of the increasing prevalence and severe effects of fire on desert shrublands and the current lack of accurate fire prediction tools. We developed a fire risk model to predict fire occurrence in a north-eastern Mojave Desert landscape. First we developed a spatial model using remote sensing data to predict fuel loads based on field estimates of fuels. We then modelled fire risk (interactions of fuel characteristics and environmental conditions conducive to wildfire) using satellite imagery, our model of fuel loads, and spatial data on ignition potential (lightning strikes and distance to roads), topography (elevation and aspect) and climate (maximum and minimum temperatures). The risk model was developed during a fire year at our study landscape and validated at a nearby landscape; model performance was accurate and similar at both sites. This study demonstrates that remote sensing techniques used in combination with field surveys can accurately predict wildfire risk in the Mojave Desert and may be applicable to other arid and semiarid lands where wildfires are prevalent.

Modeling plant composition as community continua in a forest landscape with LiDAR and hyperspectral remote sensing.

PubMed

Hakkenberg, C R; Peet, R K; Urban, D L; Song, C

2018-01-01

In light of the need to operationalize the mapping of forest composition at landscape scales, this study uses multi-scale nested vegetation sampling in conjunction with LiDAR-hyperspectral remotely sensed data from the G-LiHT airborne sensor to map vascular plant compositional turnover in a compositionally and structurally complex North Carolina Piedmont forest. Reflecting a shift in emphasis from remotely sensing individual crowns to detecting aggregate optical-structural properties of forest stands, predictive maps reflect the composition of entire vascular plant communities, inclusive of those species smaller than the resolution of the remotely sensed imagery, intertwined with proximate taxa, or otherwise obscured from optical sensors by dense upper canopies. Stand-scale vascular plant composition is modeled as community continua: where discrete community-unit classes at different compositional resolutions provide interpretable context for continuous gradient maps that depict n-dimensional compositional complexity as a single, consistent RGB color combination. In total, derived remotely sensed predictors explain 71%, 54%, and 48% of the variation in the first three components of vascular plant composition, respectively. Among all remotely sensed environmental gradients, topography derived from LiDAR ground returns, forest structure estimated from LiDAR all returns, and morphological-biochemical traits determined from hyperspectral imagery each significantly correspond to the three primary axes of floristic composition in the study site. Results confirm the complementarity of LiDAR and hyperspectral sensors for modeling the environmental gradients constraining landscape turnover in vascular plant composition and hold promise for predictive mapping applications spanning local land management to global ecosystem modeling. © 2017 by the Ecological Society of America.

Plankton Biomass Models Based on GIS and Remote Sensing Technique for Predicting Marine Megafauna Hotspots in the Solor Waters

NASA Astrophysics Data System (ADS)

Putra, MIH; Lewis, SA; Kurniasih, EM; Prabuning, D.; Faiqoh, E.

2016-11-01

Geographic information system and remote sensing techniques can be used to assist with distribution modelling; a useful tool that helps with strategic design and management plans for MPAs. This study built a pilot model of plankton biomass and distribution in the waters off Solor and Lembata, and is the first study to identify marine megafauna foraging areas in the region. Forty-three samples of zooplankton were collected every 4 km according to the range time and station of aqua MODIS. Generalized additive model (GAM) we used to modelling zooplankton biomass response from environmental properties.Thirty one samples were used to build a model of inverse distance weighting (IDW) (cell size 0.01°) and 12 samples were used as a control to verify the models accuracy. Furthermore, Getis-Ord Gi was used to identify the significance of the hotspot and cold-spot for foraging area. The GAM models was explain 88.1% response of zooplankton biomass and percent to full moon, phytopankton biomassbeing strong predictors. The sampling design was essential in order to build highly accurate models. Our models 96% accurate for phytoplankton and 88% accurate for zooplankton. The foraging behaviour was significantly related to plankton biomass hotspots, which were two times higher compared to plankton cold-spots. In addition, extremely steep slopes of the Lamakera strait support strong upwelling with highly productive waters that affect the presence of marine megafauna. This study detects that the Lamakera strait provides the planktonic requirements for marine megafauna foraging, helping to explain why this region supports such high diversity and abundance of marine megafauna.

The benefits of using remotely sensed soil moisture in parameter identification of large-scale hydrological models

NASA Astrophysics Data System (ADS)

Karssenberg, D.; Wanders, N.; de Roo, A.; de Jong, S.; Bierkens, M. F.

2013-12-01

Large-scale hydrological models are nowadays mostly calibrated using observed discharge. As a result, a large part of the hydrological system that is not directly linked to discharge, in particular the unsaturated zone, remains uncalibrated, or might be modified unrealistically. Soil moisture observations from satellites have the potential to fill this gap, as these provide the closest thing to a direct measurement of the state of the unsaturated zone, and thus are potentially useful in calibrating unsaturated zone model parameters. This is expected to result in a better identification of the complete hydrological system, potentially leading to improved forecasts of the hydrograph as well. Here we evaluate this added value of remotely sensed soil moisture in calibration of large-scale hydrological models by addressing two research questions: 1) Which parameters of hydrological models can be identified by calibration with remotely sensed soil moisture? 2) Does calibration with remotely sensed soil moisture lead to an improved calibration of hydrological models compared to approaches that calibrate only with discharge, such that this leads to improved forecasts of soil moisture content and discharge as well? To answer these questions we use a dual state and parameter ensemble Kalman filter to calibrate the hydrological model LISFLOOD for the Upper Danube area. Calibration is done with discharge and remotely sensed soil moisture acquired by AMSR-E, SMOS and ASCAT. Four scenarios are studied: no calibration (expert knowledge), calibration on discharge, calibration on remote sensing data (three satellites) and calibration on both discharge and remote sensing data. Using a split-sample approach, the model is calibrated for a period of 2 years and validated for the calibrated model parameters on a validation period of 10 years. Results show that calibration with discharge data improves the estimation of groundwater parameters (e.g., groundwater reservoir constant) and routing parameters. Calibration with only remotely sensed soil moisture results in an accurate calibration of parameters related to land surface process (e.g., the saturated conductivity of the soil), which is not possible when calibrating on discharge alone. For the upstream area up to 40000 km2, calibration on both discharge and soil moisture results in a reduction by 10-30 % in the RMSE for discharge simulations, compared to calibration on discharge alone. For discharge in the downstream area, the model performance due to assimilation of remotely sensed soil moisture is not increased or slightly decreased, most probably due to the longer relative importance of the routing and contribution of groundwater in downstream areas. When microwave soil moisture is used for calibration the RMSE of soil moisture simulations decreases from 0.072 m3m-3 to 0.062 m3m-3. The conclusion is that remotely sensed soil moisture holds potential for calibration of hydrological models leading to a better simulation of soil moisture content throughout and a better simulation of discharge in upstream areas, particularly if discharge observations are sparse.

Toward accelerating landslide mapping with interactive machine learning techniques

NASA Astrophysics Data System (ADS)

Stumpf, André; Lachiche, Nicolas; Malet, Jean-Philippe; Kerle, Norman; Puissant, Anne

2013-04-01

Despite important advances in the development of more automated methods for landslide mapping from optical remote sensing images, the elaboration of inventory maps after major triggering events still remains a tedious task. Image classification with expert defined rules typically still requires significant manual labour for the elaboration and adaption of rule sets for each particular case. Machine learning algorithm, on the contrary, have the ability to learn and identify complex image patterns from labelled examples but may require relatively large amounts of training data. In order to reduce the amount of required training data active learning has evolved as key concept to guide the sampling for applications such as document classification, genetics and remote sensing. The general underlying idea of most active learning approaches is to initialize a machine learning model with a small training set, and to subsequently exploit the model state and/or the data structure to iteratively select the most valuable samples that should be labelled by the user and added in the training set. With relatively few queries and labelled samples, an active learning strategy should ideally yield at least the same accuracy than an equivalent classifier trained with many randomly selected samples. Our study was dedicated to the development of an active learning approach for landslide mapping from VHR remote sensing images with special consideration of the spatial distribution of the samples. The developed approach is a region-based query heuristic that enables to guide the user attention towards few compact spatial batches rather than distributed points resulting in time savings of 50% and more compared to standard active learning techniques. The approach was tested with multi-temporal and multi-sensor satellite images capturing recent large scale triggering events in Brazil and China and demonstrated balanced user's and producer's accuracies between 74% and 80%. The assessment also included an experimental evaluation of the uncertainties of manual mappings from multiple experts and demonstrated strong relationships between the uncertainty of the experts and the machine learning model.

Incorporation of satellite remote sensing pan-sharpened imagery into digital soil prediction and mapping models to characterize soil property variability in small agricultural fields

NASA Astrophysics Data System (ADS)

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

2017-01-01

Soil prediction models based on spectral indices from some multispectral images are too coarse to characterize spatial pattern of soil properties in small and heterogeneous agricultural lands. Image pan-sharpening has seldom been utilized in Digital Soil Mapping research before. This research aimed to analyze the effects of pan-sharpened (PAN) remote sensing spectral indices on soil prediction models in smallholder farm settings. This research fused the panchromatic band and multispectral (MS) bands of WorldView-2, GeoEye-1, and Landsat 8 images in a village in Southern India by Brovey, Gram-Schmidt and Intensity-Hue-Saturation methods. Random Forest was utilized to develop soil total nitrogen (TN) and soil exchangeable potassium (Kex) prediction models by incorporating multiple spectral indices from the PAN and MS images. Overall, our results showed that PAN remote sensing spectral indices have similar spectral characteristics with soil TN and Kex as MS remote sensing spectral indices. There is no soil prediction model incorporating the specific type of pan-sharpened spectral indices always had the strongest prediction capability of soil TN and Kex. The incorporation of pan-sharpened remote sensing spectral data not only increased the spatial resolution of the soil prediction maps, but also enhanced the prediction accuracy of soil prediction models. Small farms with limited footprint, fragmented ownership and diverse crop cycle should benefit greatly from the pan-sharpened high spatial resolution imagery for soil property mapping. Our results show that multiple high and medium resolution images can be used to map soil properties suggesting the possibility of an improvement in the maps' update frequency. Additionally, the results should benefit the large agricultural community through the reduction of routine soil sampling cost and improved prediction accuracy.

Calibration of Noah soil hydraulic property parameters using surface soil moisture from SMOS and basin-wide in situ observations

USDA-ARS?s Scientific Manuscript database

Soil hydraulic properties can be retrieved from physical sampling of soil, via surveys, but this is time consuming and only as accurate as the scale of the sample. Remote sensing provides an opportunity to get pertinent soil properties at large scales, which is very useful for large scale modeling....

Construction of a remotely sensed area sampling frame for Southern Brazil

NASA Technical Reports Server (NTRS)

Fecso, R.; Gardner, W.; Hale, B.; Johnson, V.; Pavlasek, S. (Principal Investigator)

1982-01-01

A remotely sensed area sampling frame was constructed for selected areas in Southern Brazil. The sampling unit information was stored in digital form in a latitudinal/longitudinal characterized population. Computerized sampling procedures were developed which allow for flexibility in sample unit specifications and sampling designs.

Per-pixel bias-variance decomposition of continuous errors in data-driven geospatial modeling: A case study in environmental remote sensing

NASA Astrophysics Data System (ADS)

Gao, Jing; Burt, James E.

2017-12-01

This study investigates the usefulness of a per-pixel bias-variance error decomposition (BVD) for understanding and improving spatially-explicit data-driven models of continuous variables in environmental remote sensing (ERS). BVD is a model evaluation method originated from machine learning and have not been examined for ERS applications. Demonstrated with a showcase regression tree model mapping land imperviousness (0-100%) using Landsat images, our results showed that BVD can reveal sources of estimation errors, map how these sources vary across space, reveal the effects of various model characteristics on estimation accuracy, and enable in-depth comparison of different error metrics. Specifically, BVD bias maps can help analysts identify and delineate model spatial non-stationarity; BVD variance maps can indicate potential effects of ensemble methods (e.g. bagging), and inform efficient training sample allocation - training samples should capture the full complexity of the modeled process, and more samples should be allocated to regions with more complex underlying processes rather than regions covering larger areas. Through examining the relationships between model characteristics and their effects on estimation accuracy revealed by BVD for both absolute and squared errors (i.e. error is the absolute or the squared value of the difference between observation and estimate), we found that the two error metrics embody different diagnostic emphases, can lead to different conclusions about the same model, and may suggest different solutions for performance improvement. We emphasize BVD's strength in revealing the connection between model characteristics and estimation accuracy, as understanding this relationship empowers analysts to effectively steer performance through model adjustments.

Quantifying Aerial Concentrations of Maize Pollen in the Atmospheric Surface Layer Using Remote-Piloted Airplanes and Lagrangian Stochastic Modeling

NASA Astrophysics Data System (ADS)

Aylor, Donald E.; Boehm, Matthew T.; Shields, Elson J.

2006-07-01

The extensive adoption of genetically modified crops has led to a need to understand better the dispersal of pollen in the atmosphere because of the potential for unwanted movement of genetic traits via pollen flow in the environment. The aerial dispersal of maize pollen was studied by comparing the results of a Lagrangian stochastic (LS) model with pollen concentration measurements made over cornfields using a combination of tower-based rotorod samplers and airborne radio-controlled remote-piloted vehicles (RPVs) outfitted with remotely operated pollen samplers. The comparison between model and measurements was conducted in two steps. In the first step, the LS model was used in combination with the rotorod samplers to estimate the pollen release rate Q for each sampling period. In the second step, a modeled value for the concentration Cmodel, corresponding to each RPV measured value Cmeasure, was calculated by simulating the RPV flight path through the LS model pollen plume corresponding to the atmospheric conditions, field geometry, wind direction, and source strength. The geometric mean and geometric standard deviation of the ratio Cmodel/Cmeasure over all of the sampling periods, except those determined to be upwind of the field, were 1.42 and 4.53, respectively, and the lognormal distribution corresponding to these values was found to fit closely the PDF of Cmodel/Cmeasure. Model output was sensitive to the turbulence parameters, with a factor-of-100 difference in the average value of Cmodel over the range of values encountered during the experiment. In comparison with this large potential variability, it is concluded that the average factor of 1.4 between Cmodel and Cmeasure found here indicates that the LS model is capable of accurately predicting, on average, concentrations over a range of atmospheric conditions.

[Review of estimation on oceanic primary productivity by using remote sensing methods.

PubMed

Xu, Hong Yun; Zhou, Wei Feng; Ji, Shi Jian

2016-09-01

Accuracy estimation of oceanic primary productivity is of great significance in the assessment and management of fisheries resources, marine ecology systems, global change and other fields. The traditional measurement and estimation of oceanic primary productivity has to rely on in situ sample data by vessels. Satellite remote sensing has advantages of providing dynamic and eco-environmental parameters of ocean surface at large scale in real time. Thus, satellite remote sensing has increasingly become an important means for oceanic primary productivity estimation on large spatio-temporal scale. Combining with the development of ocean color sensors, the models to estimate the oceanic primary productivity by satellite remote sensing have been developed that could be mainly summarized as chlorophyll-based, carbon-based and phytoplankton absorption-based approach. The flexibility and complexity of the three kinds of models were presented in the paper. On this basis, the current research status for global estimation of oceanic primary productivity was analyzed and evaluated. In view of these, four research fields needed to be strengthened in further stu-dy: 1) Global oceanic primary productivity estimation should be segmented and studied, 2) to dee-pen the research on absorption coefficient of phytoplankton, 3) to enhance the technology of ocea-nic remote sensing, 4) to improve the in situ measurement of primary productivity.

[Crop geometry identification based on inversion of semiempirical BRDF models].

PubMed

Zhao, Chun-jiang; Huang, Wen-jiang; Mu, Xu-han; Wang, Jin-diz; Wang, Ji-hua

2009-09-01

With the rapid development of remote sensing technology, the application of remote sensing has extended from single view angle to multi-view angles. It was studied for the qualitative and quantitative effect of average leaf angle (ALA) on crop canopy reflected spectrum. Effect of ALA on canopy reflected spectrum can not be ignored with inversion of leaf area index (LAI) and monitoring of crop growth condition by remote sensing technology. Investigations of the effect of erective and horizontal varieties were conducted by bidirectional canopy reflected spectrum and semiempirical bidirectional reflectance distribution function (BRDF) models. The sensitive analysis was done based on the weight for the volumetric kernel (fvol), the weight for the geometric kernel (fgeo), and the weight for constant corresponding to isotropic reflectance (fiso) at red band (680 nm) and near infrared band (800 nm). By combining the weights of the red and near-infrared bands, the semiempirical models can obtain structural information by retrieving biophysical parameters from the physical BRDF model and a number of bidirectional observations. So, it will allow an on-site and non-sampling mode of crop ALA identification, which is useful for using remote sensing for crop growth monitoring and for improving the LAI inversion accuracy, and it will help the farmers in guiding the fertilizer and irrigation management in the farmland without a priori knowledge.

Filtering method of star control points for geometric correction of remote sensing image based on RANSAC algorithm

NASA Astrophysics Data System (ADS)

Tan, Xiangli; Yang, Jungang; Deng, Xinpu

2018-04-01

In the process of geometric correction of remote sensing image, occasionally, a large number of redundant control points may result in low correction accuracy. In order to solve this problem, a control points filtering algorithm based on RANdom SAmple Consensus (RANSAC) was proposed. The basic idea of the RANSAC algorithm is that using the smallest data set possible to estimate the model parameters and then enlarge this set with consistent data points. In this paper, unlike traditional methods of geometric correction using Ground Control Points (GCPs), the simulation experiments are carried out to correct remote sensing images, which using visible stars as control points. In addition, the accuracy of geometric correction without Star Control Points (SCPs) optimization is also shown. The experimental results show that the SCPs's filtering method based on RANSAC algorithm has a great improvement on the accuracy of remote sensing image correction.

Water Quality Variable Estimation using Partial Least Squares Regression and Multi-Scale Remote Sensing.

NASA Astrophysics Data System (ADS)

Peterson, K. T.; Wulamu, A.

2017-12-01

Water, essential to all living organisms, is one of the Earth's most precious resources. Remote sensing offers an ideal approach to monitor water quality over traditional in-situ techniques that are highly time and resource consuming. Utilizing a multi-scale approach, incorporating data from handheld spectroscopy, UAS based hyperspectal, and satellite multispectral images were collected in coordination with in-situ water quality samples for the two midwestern watersheds. The remote sensing data was modeled and correlated to the in-situ water quality variables including chlorophyll content (Chl), turbidity, and total dissolved solids (TDS) using Normalized Difference Spectral Indices (NDSI) and Partial Least Squares Regression (PLSR). The results of the study supported the original hypothesis that correlating water quality variables with remotely sensed data benefits greatly from the use of more complex modeling and regression techniques such as PLSR. The final results generated from the PLSR analysis resulted in much higher R2 values for all variables when compared to NDSI. The combination of NDSI and PLSR analysis also identified key wavelengths for identification that aligned with previous study's findings. This research displays the advantages and future for complex modeling and machine learning techniques to improve water quality variable estimation from spectral data.

A strategy for the observation of volcanism on Earth from space.

PubMed

Wadge, G

2003-01-15

Heat, strain, topography and atmospheric emissions associated with volcanism are well observed by satellites orbiting the Earth. Gravity and electromagnetic transients from volcanoes may also prove to be measurable from space. The nature of eruptions means that the best strategy for measuring their dynamic properties remotely from space is to employ two modes with different spatial and temporal samplings: eruption mode and background mode. Such observational programmes are best carried out at local or regional volcano observatories by coupling them with numerical models of volcanic processes. Eventually, such models could become multi-process, operational forecast models that assimilate the remote and other observables to constrain their uncertainties. The threat posed by very large magnitude explosive eruptions is global and best addressed by a spaceborne observational programme with a global remit.

Sampling Errors in Monthly Rainfall Totals for TRMM and SSM/I, Based on Statistics of Retrieved Rain Rates and Simple Models

NASA Technical Reports Server (NTRS)

Bell, Thomas L.; Kundu, Prasun K.; Einaudi, Franco (Technical Monitor)

2000-01-01

Estimates from TRMM satellite data of monthly total rainfall over an area are subject to substantial sampling errors due to the limited number of visits to the area by the satellite during the month. Quantitative comparisons of TRMM averages with data collected by other satellites and by ground-based systems require some estimate of the size of this sampling error. A method of estimating this sampling error based on the actual statistics of the TRMM observations and on some modeling work has been developed. "Sampling error" in TRMM monthly averages is defined here relative to the monthly total a hypothetical satellite permanently stationed above the area would have reported. "Sampling error" therefore includes contributions from the random and systematic errors introduced by the satellite remote sensing system. As part of our long-term goal of providing error estimates for each grid point accessible to the TRMM instruments, sampling error estimates for TRMM based on rain retrievals from TRMM microwave (TMI) data are compared for different times of the year and different oceanic areas (to minimize changes in the statistics due to algorithmic differences over land and ocean). Changes in sampling error estimates due to changes in rain statistics due 1) to evolution of the official algorithms used to process the data, and 2) differences from other remote sensing systems such as the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I), are analyzed.

Improving snow density estimation for mapping SWE with Lidar snow depth: assessment of uncertainty in modeled density and field sampling strategies in NASA SnowEx

NASA Astrophysics Data System (ADS)

Raleigh, M. S.; Smyth, E.; Small, E. E.

2017-12-01

The spatial distribution of snow water equivalent (SWE) is not sufficiently monitored with either remotely sensed or ground-based observations for water resources management. Recent applications of airborne Lidar have yielded basin-wide mapping of SWE when combined with a snow density model. However, in the absence of snow density observations, the uncertainty in these SWE maps is dominated by uncertainty in modeled snow density rather than in Lidar measurement of snow depth. Available observations tend to have a bias in physiographic regime (e.g., flat open areas) and are often insufficient in number to support testing of models across a range of conditions. Thus, there is a need for targeted sampling strategies and controlled model experiments to understand where and why different snow density models diverge. This will enable identification of robust model structures that represent dominant processes controlling snow densification, in support of basin-scale estimation of SWE with remotely-sensed snow depth datasets. The NASA SnowEx mission is a unique opportunity to evaluate sampling strategies of snow density and to quantify and reduce uncertainty in modeled snow density. In this presentation, we present initial field data analyses and modeling results over the Colorado SnowEx domain in the 2016-2017 winter campaign. We detail a framework for spatially mapping the uncertainty in snowpack density, as represented across multiple models. Leveraging the modular SUMMA model, we construct a series of physically-based models to assess systematically the importance of specific process representations to snow density estimates. We will show how models and snow pit observations characterize snow density variations with forest cover in the SnowEx domains. Finally, we will use the spatial maps of density uncertainty to evaluate the selected locations of snow pits, thereby assessing the adequacy of the sampling strategy for targeting uncertainty in modeled snow density.

A preliminary study of the statistical analyses and sampling strategies associated with the integration of remote sensing capabilities into the current agricultural crop forecasting system

NASA Technical Reports Server (NTRS)

Sand, F.; Christie, R.

1975-01-01

Extending the crop survey application of remote sensing from small experimental regions to state and national levels requires that a sample of agricultural fields be chosen for remote sensing of crop acreage, and that a statistical estimate be formulated with measurable characteristics. The critical requirements for the success of the application are reviewed in this report. The problem of sampling in the presence of cloud cover is discussed. Integration of remotely sensed information about crops into current agricultural crop forecasting systems is treated on the basis of the USDA multiple frame survey concepts, with an assumed addition of a new frame derived from remote sensing. Evolution of a crop forecasting system which utilizes LANDSAT and future remote sensing systems is projected for the 1975-1990 time frame.

Modelling population distribution using remote sensing imagery and location-based data

NASA Astrophysics Data System (ADS)

Song, J.; Prishchepov, A. V.

2017-12-01

Detailed spatial distribution of population density is essential for city studies such as urban planning, environmental pollution and city emergency, even estimate pressure on the environment and human exposure and risks to health. However, most of the researches used census data as the detailed dynamic population distribution are difficult to acquire, especially in microscale research. This research describes a method using remote sensing imagery and location-based data to model population distribution at the function zone level. Firstly, urban functional zones within a city were mapped by high-resolution remote sensing images and POIs. The workflow of functional zones extraction includes five parts: (1) Urban land use classification. (2) Segmenting images in built-up area. (3) Identification of functional segments by POIs. (4) Identification of functional blocks by functional segmentation and weight coefficients. (5) Assessing accuracy by validation points. The result showed as Fig.1. Secondly, we applied ordinary least square and geographically weighted regression to assess spatial nonstationary relationship between light digital number (DN) and population density of sampling points. The two methods were employed to predict the population distribution over the research area. The R²of GWR model were in the order of 0.7 and typically showed significant variations over the region than traditional OLS model. The result showed as Fig.2.Validation with sampling points of population density demonstrated that the result predicted by the GWR model correlated well with light value. The result showed as Fig.3. Results showed: (1) Population density is not linear correlated with light brightness using global model. (2) VIIRS night-time light data could estimate population density integrating functional zones at city level. (3) GWR is a robust model to map population distribution, the adjusted R2 of corresponding GWR models were higher than the optimal OLS models, confirming that GWR models demonstrate better prediction accuracy. So this method provide detailed population density information for microscale citizen studies.

Pairing field methods to improve inference in wildlife surveys while accommodating detection covariance.

PubMed

Clare, John; McKinney, Shawn T; DePue, John E; Loftin, Cynthia S

2017-10-01

It is common to use multiple field sampling methods when implementing wildlife surveys to compare method efficacy or cost efficiency, integrate distinct pieces of information provided by separate methods, or evaluate method-specific biases and misclassification error. Existing models that combine information from multiple field methods or sampling devices permit rigorous comparison of method-specific detection parameters, enable estimation of additional parameters such as false-positive detection probability, and improve occurrence or abundance estimates, but with the assumption that the separate sampling methods produce detections independently of one another. This assumption is tenuous if methods are paired or deployed in close proximity simultaneously, a common practice that reduces the additional effort required to implement multiple methods and reduces the risk that differences between method-specific detection parameters are confounded by other environmental factors. We develop occupancy and spatial capture-recapture models that permit covariance between the detections produced by different methods, use simulation to compare estimator performance of the new models to models assuming independence, and provide an empirical application based on American marten (Martes americana) surveys using paired remote cameras, hair catches, and snow tracking. Simulation results indicate existing models that assume that methods independently detect organisms produce biased parameter estimates and substantially understate estimate uncertainty when this assumption is violated, while our reformulated models are robust to either methodological independence or covariance. Empirical results suggested that remote cameras and snow tracking had comparable probability of detecting present martens, but that snow tracking also produced false-positive marten detections that could potentially substantially bias distribution estimates if not corrected for. Remote cameras detected marten individuals more readily than passive hair catches. Inability to photographically distinguish individual sex did not appear to induce negative bias in camera density estimates; instead, hair catches appeared to produce detection competition between individuals that may have been a source of negative bias. Our model reformulations broaden the range of circumstances in which analyses incorporating multiple sources of information can be robustly used, and our empirical results demonstrate that using multiple field-methods can enhance inferences regarding ecological parameters of interest and improve understanding of how reliably survey methods sample these parameters. © 2017 by the Ecological Society of America.

Ground based remote sensing and physiological measurements provide novel insights into canopy photosynthetic optimization in arctic shrubs

NASA Astrophysics Data System (ADS)

Magney, T. S.; Griffin, K. L.; Boelman, N.; Eitel, J.; Greaves, H.; Prager, C.; Logan, B.; Oliver, R.; Fortin, L.; Vierling, L. A.

2014-12-01

Because changes in vegetation structure and function in the Arctic are rapid and highly dynamic phenomena, efforts to understand the C balance of the tundra require repeatable, objective, and accurate remote sensing methods for estimating aboveground C pools and fluxes over large areas. A key challenge addressing the modelling of aboveground C is to utilize process-level information from fine-scale studies. Utilizing information obtained from high resolution remote sensing systems could help to better understand the C source/sink strength of the tundra, which will in part depend on changes in photosynthesis resulting from the partitioning of photosynthetic machinery within and among deciduous shrub canopies. Terrestrial LiDAR and passive hyperspectral remote sensing measurements offer an effective, repeatable, and scalable method to understand photosynthetic performance and partitioning at the canopy scale previously unexplored in arctic systems. Using a 3-D shrub canopy model derived from LiDAR, we quantified the light regime of leaves within shrub canopies to gain a better understanding of how light interception varies in response to the Arctic's complex radiation regime. This information was then coupled with pigment sampling (i.e., xanthophylls, and Chl a/b) to evaluate the optimization of foliage photosynthetic capacity within shrub canopies due to light availability. In addition, a lab experiment was performed to validate evidence of canopy level optimization via gradients of light intensity and leaf light environment. For this, hyperspectral reflectance (photochemical reflectance index (PRI)), and solar induced fluorescence (SIF)) was collected in conjunction with destructive pigment samples (xanthophylls) and chlorophyll fluorescence measurements in both sunlit and shaded canopy positions.

Remote Sensing of Suspended Sediment Dynamics in the Mississippi Sound

NASA Astrophysics Data System (ADS)

Merritt, D. N.; Skarke, A. D.; Silwal, S.; Dash, P.

2016-02-01

The Mississippi Sound is a semi-enclosed estuary between the coast of Mississippi and a chain of offshore barrier islands with relatively shallow water depths and high marine biodiversity that is wildly utilized for commercial fishing and public recreation. The discharge of sediment-laden rivers into the Mississippi Sound and the adjacent Northern Gulf of Mexico creates turbid plumes that can extend hundreds of square kilometers along the coast and persist for multiple days. The concentration of suspended sediment in these coastal waters is an important parameter in the calculation of regional sediment budgets as well as analysis of water-quality factors such as primary productivity, nutrient dynamics, and the transport of pollutants as well as pathogens. The spectral resolution, sampling frequency, and regional scale spatial domain associated with satellite based sensors makes remote sensing an ideal tool to monitor suspended sediment dynamics in the Northern Gulf of Mexico. Accordingly, the presented research evaluates the validity of published models that relate remote sensing reflectance with suspended sediment concentrations (SSC), for similar environmental settings, with 51 in situ observations of SSC from the Mississippi Sound. Additionally, regression analysis is used to correlate additional in situ observations of SSC in Mississippi Sound with coincident observations of visible and near-infrared band reflectance collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Aqua satellite, in order to develop a site-specific empirical predictive model for SSC. Finally, specific parameters of the sampled suspended sediment such as grain size and mineralogy are analyzed in order to quantify their respective contributions to total remotely sensed reflectance.

Remote laboratories for optical metrology: from the lab to the cloud

NASA Astrophysics Data System (ADS)

Osten, W.; Wilke, M.; Pedrini, G.

2012-10-01

The idea of remote and virtual metrology has been reported already in 2000 with a conceptual illustration by use of comparative digital holography, aimed at the comparison of two nominally identical but physically different objects, e.g., master and sample, in industrial inspection processes. However, the concept of remote and virtual metrology can be extended far beyond this. For example, it does not only allow for the transmission of static holograms over the Internet, but also provides an opportunity to communicate with and eventually control the physical set-up of a remote metrology system. Furthermore, the metrology system can be modeled in the environment of a 3D virtual reality using CAD or similar technology, providing a more intuitive interface to the physical setup within the virtual world. An engineer or scientist who would like to access the remote real world system can log on to the virtual system, moving and manipulating the setup through an avatar and take the desired measurements. The real metrology system responds to the interaction between the avatar and the 3D virtual representation, providing a more intuitive interface to the physical setup within the virtual world. The measurement data are stored and interpreted automatically for appropriate display within the virtual world, providing the necessary feedback to the experimenter. Such a system opens up many novel opportunities in industrial inspection such as the remote master-sample-comparison and the virtual assembling of parts that are fabricated at different places. Moreover, a multitude of new techniques can be envisaged. To them belong modern ways for documenting, efficient methods for metadata storage, the possibility for remote reviewing of experimental results, the adding of real experiments to publications by providing remote access to the metadata and to the experimental setup via Internet, the presentation of complex experiments in classrooms and lecture halls, the sharing of expensive and complex infrastructure within international collaborations, the implementation of new ways for the remote test of new devices, for their maintenance and service, and many more. The paper describes the idea of remote laboratories and illustrates the potential of the approach on selected examples with special attention to optical metrology.

Seasonal changes in the tropospheric carbon monoxide profile over the remote Southern Hemisphere evaluated using multi-model simulations and aircraft observations

NASA Astrophysics Data System (ADS)

Fisher, J. A.; Wilson, S. R.; Zeng, G.; Williams, J. E.; Emmons, L. K.; Langenfelds, R. L.; Krummel, P. B.; Steele, L. P.

2014-11-01

We use aircraft observations from the 1991-2000 Cape Grim Overflight Program and the 2009-2011 HIAPER Pole-to-Pole Observations (HIPPO), together with output from four chemical transport and chemistry-climate models, to better understand the vertical distribution of carbon monoxide (CO) in the remote Southern Hemisphere. Observed CO vertical gradients at Cape Grim vary from 1.6 ppbv km-1 in austral autumn to 2.2 ppbv km-1 in austral spring. CO vertical profiles from Cape Grim are remarkably consistent with those observed over the southern mid-latitudes Pacific during HIPPO, despite major differences in time periods, flight locations, and sampling strategies between the two datasets. Using multi-model simulations from the Southern Hemisphere Model Intercomparison Project (SHMIP), we find that observed CO vertical gradients in austral winter-spring are well-represented in models and can be attributed to primary CO emissions from biomass burning. In austral summer-autumn, inter-model variability in simulated gradients is much larger, and two of the four SHMIP models significantly underestimate the Cape Grim observations. Sensitivity simulations show that CO vertical gradients at this time of year are driven by long-range transport of secondary CO of biogenic origin, implying a large sensitivity of the remote Southern Hemisphere troposphere to biogenic emissions and chemistry. Inter-model variability in summer-autumn gradients can be explained by differences in both the chemical mechanisms that drive secondary production of CO from biogenic sources and the vertical transport that redistributes this CO throughout the Southern Hemisphere. This suggests that the CO vertical gradient in the remote Southern Hemisphere provides a sensitive test of the chemistry and transport processes that define the chemical state of the background atmosphere.

Fiber-optic apparatus and method for measurement of luminescence and raman scattering

DOEpatents

Myrick, Michael L.; Angel, Stanley M.

1993-01-01

A dual fiber forward scattering optrode for Raman spectroscopy with the remote ends of the fibers in opposed, spaced relationship to each other to form a analyte sampling space therebetween and the method of measuring Raman spectra utilizing same. One optical fiber is for sending an exciting signal to the remote sampling space and, at its remote end, has a collimating microlens and an optical filter for filtering out background emissions generated in the fiber. The other optical fiber is for collecting the Raman scattering signal at the remote sampling space and, at its remote end, has a collimating microlens and an optical filter to prevent the exciting signal from the exciting fiber from entering the collection fiber and to thereby prevent the generation of background emissions in the collecting fiber.

At-sea detection of marine debris: overview of technologies, processes, issues, and options.

PubMed

Mace, Thomas H

2012-01-01

At-sea detection of marine debris presents a difficult problem, as the debris items are often relatively small and partially submerged. However, they may accumulate in water parcel boundaries or eddy lines. The application of models, satellite radar and multispectral data, and airborne remote sensing (particularly radar) to focus the search on eddies and convergence zones in the open ocean appear to be a productive avenue of investigation. A multistage modeling and remote sensing approach is proposed for the identification of areas of the open ocean where debris items are more likely to congregate. A path forward may best be achieved through the refinement of the Ghost Net procedures with the addition of a final search stage using airborne radar from an UAS simulator aircraft to detect zones of potential accumulation for direct search. Sampling strategies, direct versus indirect measurements, remote sensing resolution, sensor/platform considerations, and future state are addressed. Copyright © 2011 Elsevier Ltd. All rights reserved.

Active machine learning for rapid landslide inventory mapping with VHR satellite images (Invited)

NASA Astrophysics Data System (ADS)

Stumpf, A.; Lachiche, N.; Malet, J.; Kerle, N.; Puissant, A.

2013-12-01

VHR satellite images have become a primary source for landslide inventory mapping after major triggering events such as earthquakes and heavy rainfalls. Visual image interpretation is still the prevailing standard method for operational purposes but is time-consuming and not well suited to fully exploit the increasingly better supply of remote sensing data. Recent studies have addressed the development of more automated image analysis workflows for landslide inventory mapping. In particular object-oriented approaches that account for spatial and textural image information have been demonstrated to be more adequate than pixel-based classification but manually elaborated rule-based classifiers are difficult to adapt under changing scene characteristics. Machine learning algorithm allow learning classification rules for complex image patterns from labelled examples and can be adapted straightforwardly with available training data. In order to reduce the amount of costly training data active learning (AL) has evolved as a key concept to guide the sampling for many applications. The underlying idea of AL is to initialize a machine learning model with a small training set, and to subsequently exploit the model state and data structure to iteratively select the most valuable samples that should be labelled by the user. With relatively few queries and labelled samples, an AL strategy yields higher accuracies than an equivalent classifier trained with many randomly selected samples. This study addressed the development of an AL method for landslide mapping from VHR remote sensing images with special consideration of the spatial distribution of the samples. Our approach [1] is based on the Random Forest algorithm and considers the classifier uncertainty as well as the variance of potential sampling regions to guide the user towards the most valuable sampling areas. The algorithm explicitly searches for compact regions and thereby avoids a spatially disperse sampling pattern inherent to most other AL methods. The accuracy, the sampling time and the computational runtime of the algorithm were evaluated on multiple satellite images capturing recent large scale landslide events. Sampling between 1-4% of the study areas the accuracies between 74% and 80% were achieved, whereas standard sampling schemes yielded only accuracies between 28% and 50% with equal sampling costs. Compared to commonly used point-wise AL algorithm the proposed approach significantly reduces the number of iterations and hence the computational runtime. Since the user can focus on relatively few compact areas (rather than on hundreds of distributed points) the overall labeling time is reduced by more than 50% compared to point-wise queries. An experimental evaluation of multiple expert mappings demonstrated strong relationships between the uncertainties of the experts and the machine learning model. It revealed that the achieved accuracies are within the range of the inter-expert disagreement and that it will be indispensable to consider ground truth uncertainties to truly achieve further enhancements in the future. The proposed method is generally applicable to a wide range of optical satellite images and landslide types. [1] A. Stumpf, N. Lachiche, J.-P. Malet, N. Kerle, and A. Puissant, Active learning in the spatial domain for remote sensing image classification, IEEE Transactions on Geosciece and Remote Sensing. 2013, DOI 10.1109/TGRS.2013.2262052.

Forest inventory predictions from individual tree crowns: regression modeling within a sample framework

Treesearch

James W. Flewelling

2009-01-01

Remotely sensed data can be used to make digital maps showing individual tree crowns (ITC) for entire forests. Attributes of the ITCs may include area, shape, height, and color. The crown map is sampled in a way that provides an unbiased linkage between ITCs and identifiable trees measured on the ground. Methods of avoiding edge bias are given. In an example from a...

Mapping Surface Water DOC in the Northern Gulf of Mexico Using CDOM Absorption Coefficients and Remote Sensing Imagery

NASA Astrophysics Data System (ADS)

Kelly, B.; Chelsky, A.; Bulygina, E.; Roberts, B. J.

2017-12-01

Remote sensing techniques have become valuable tools to researchers, providing the capability to measure and visualize important parameters without the need for time or resource intensive sampling trips. Relationships between dissolved organic carbon (DOC), colored dissolved organic matter (CDOM) and spectral data have been used to remotely sense DOC concentrations in riverine systems, however, this approach has not been applied to the northern Gulf of Mexico (GoM) and needs to be tested to determine how accurate these relationships are in riverine-dominated shelf systems. In April, July, and October 2017 we sampled surface water from 80+ sites over an area of 100,000 km2 along the Louisiana-Texas shelf in the northern GoM. DOC concentrations were measured on filtered water samples using a Shimadzu TOC-VCSH analyzer using standard techniques. Additionally, DOC concentrations were estimated from CDOM absorption coefficients of filtered water samples on a UV-Vis spectrophotometer using a modification of the methods of Fichot and Benner (2011). These values were regressed against Landsat visible band spectral data for those same locations to establish a relationship between the spectral data, CDOM absorption coefficients. This allowed us to spatially map CDOM absorption coefficients in the Gulf of Mexico using the Landsat spectral data in GIS. We then used a multiple linear regressions model to derive DOC concentrations from the CDOM absorption coefficients and applied those to our map. This study provides an evaluation of the viability of scaling up CDOM absorption coefficient and remote-sensing derived estimates of DOC concentrations to the scale of the LA-TX shelf ecosystem.

Monitoring spatial variations in soil organic carbon using remote sensing and geographic information systems

NASA Astrophysics Data System (ADS)

Jaber, Salahuddin M.

Soil organic carbon (SOC) sequestration is a component of larger strategies to control the accumulation of greenhouse gases that may be causing global warming. To implement this approach, it is necessary to improve the methods of measuring SOC content. Among these methods are indirect remote sensing and geographic information systems (GIS) techniques that are required to provide non-intrusive, low cost, and spatially continuous information that cover large areas on a repetitive basis. The main goal of this study is to evaluate the effects of using Hyperion hyperspectral data on improving the existing remote sensing and GIS-based methodologies for rapidly, efficiently, and accurately measuring SOC content on farmland. The study area is Big Creek Watershed (BCW) in Southern Illinois. The methodology consists of compiling a GIS database (consisting of remote sensing and soil variables) for 303 composite soil samples collected from representative pixels along the Hyperion coverage area of the watershed. Stepwise procedures were used to calibrate and validate linear multiple regression models where SOC was regarded as the response and the other remote sensing and soil variables as the predictors. Two models were selected. The first was the best all variables model and the second was the best only raster variables model. Map algebra was implemented to extrapolate the best only raster variables model and produce a SOC map for the BGW. This study concluded that Hyperion data marginally improved the predictability of the existing SOC statistical models based on multispectral satellite remote sensing sensors with correlation coefficient of 0.37 and root mean square error of 3.19 metric tons/hectare to a 15-cm depth. The total SOC pool of the study area is about 225,232 metric tons to 15-cm depth. The nonforested wetlands contained the highest SOC density (34.3 metric tons/hectare/15cm) with total SOC content of about 2,003.5 metric tons to 15-cm depth, where croplands had the lowest SOC density (21.6 metric tons/hectare/15cm) with total SOC content of about 44,571.2 metric tons to 15-cm depth.

Proceedings of the third annual forest inventory and analysis symposium; 2001 October 17-19; Traverse City, Michigan

Treesearch

Ronald E. McRoberts; Gregory A. Reams; Paul C. Van Deusen; John W. Moser

2003-01-01

Documents contributions to forest inventory in the areas of sampling, remote sensing, modeling, information management, and analysis with emphasis on implementation of the annual inventory system of the Forest Inventory and Analysis program of the USDA Forest Service.

Antibiotic-Resistant Escherichia coli in Migratory Birds Inhabiting Remote Alaska.

PubMed

Ramey, Andrew M; Hernandez, Jorge; Tyrlöv, Veronica; Uher-Koch, Brian D; Schmutz, Joel A; Atterby, Clara; Järhult, Josef D; Bonnedahl, Jonas

2017-12-11

We explored the abundance of antibiotic-resistant Escherichia coli among migratory birds at remote sites in Alaska and used a comparative approach to speculate on plausible explanations for differences in detection among species. At a remote island site, we detected antibiotic-resistant E. coli phenotypes in samples collected from glaucous-winged gulls (Larus glaucescens), a species often associated with foraging at landfills, but not in samples collected from black-legged kittiwakes (Rissa tridactyla), a more pelagic gull that typically inhabits remote areas year-round. We did not find evidence for antibiotic-resistant E. coli among 347 samples collected primarily from waterfowl at a second remote site in western Alaska. Our results provide evidence that glaucous-winged gulls may be more likely to be infected with antibiotic-resistant E. coli at remote breeding sites as compared to sympatric black-legged kittiwakes. This could be a function of the tendency of glaucous-winged gulls to forage at landfills where antibiotic-resistant bacterial infections may be acquired and subsequently dispersed. The low overall detection of antibiotic-resistant E. coli in migratory birds sampled at remote sites in Alaska is consistent with the premise that anthropogenic inputs into the local environment or the relative lack thereof influences the prevalence of antibiotic-resistant bacteria among birds inhabiting the area.

A new method to sample stuttering in preschool children.

PubMed

O'Brian, Sue; Jones, Mark; Pilowsky, Rachel; Onslow, Mark; Packman, Ann; Menzies, Ross

2010-06-01

This study reports a new method for sampling the speech of preschool stuttering children outside the clinic environment. Twenty parents engaged their stuttering children in an everyday play activity in the home with a telephone handset nearby. A remotely located researcher telephoned the parent and recorded the play session with a phone-recording jack attached to a digital audio recorder at the remote location. The parent placed an audio recorder near the child for comparison purposes. Children as young as 2 years complied with the remote method of speech sampling. The quality of the remote recordings was superior to that of the in-home recordings. There was no difference in means or reliability of stutter-count measures made from the remote recordings compared with those made in-home. Advantages of the new method include: (1) cost efficiency of real-time measurement of percent syllables stuttered in naturalistic situations, (2) reduction of bias associated with parent-selected timing of home recordings, (3) standardization of speech sampling procedures, (4) improved parent compliance with sampling procedures, (5) clinician or researcher on-line control of the acoustic and linguistic quality of recordings, and (6) elimination of the need to lend equipment to parents for speech sampling.

Ground-based grasslands data to support remote sensing and ecosystem modeling of terrestrial primary production

NASA Technical Reports Server (NTRS)

Olson, R. J.; Scurlock, J. M. O.; Turner, R. S.; Jennings, S. V.

1995-01-01

Estimating terrestrial net primary production (NPP) using remote-sensing tools and ecosystem models requires adequate ground-based measurements for calibration, parameterization, and validation. These data needs were strongly endorsed at a recent meeting of ecosystem modelers organized by the International Geosphere-Biosphere Program's (IGBP's) Data and Information System (DIS) and its Global Analysis, Interpretation, and Modelling (GAIM) Task Force. To meet these needs, a multinational, multiagency project is being coordinated by the IGBP DIS to compile existing NPP data from field sites and to regionalize NPP point estimates to various-sized grid cells. Progress at Oak Ridge National Laboratory (ORNL) on compiling NPP data for grasslands as part of the IGBP DIS data initiative is described. Site data and associated documentation from diverse field studies are being acquired for selected grasslands and are being reviewed for completeness, consistency, and adequacy of documentation, including a description of sampling methods. Data are being compiled in a database with spatial, temporal, and thematic characteristics relevant to remote sensing and global modeling. NPP data are available from the ORNL Distributed Active Archive Center (DAAC) for biogeochemical dynamics. The ORNL DAAC is part of the Earth Observing System Data and Information System, of the US National Aeronautics and Space Administration.

Remote-sensing reflectance determinations in the coastal ocean environment: impact of instrumental characteristics and environmental variability.

PubMed

Toole, D A; Siegel, D A; Menzies, D W; Neumann, M J; Smith, R C

2000-01-20

Three independent ocean color sampling methodologies are compared to assess the potential impact of instrumental characteristics and environmental variability on shipboard remote-sensing reflectance observations from the Santa Barbara Channel, California. Results indicate that under typical field conditions, simultaneous determinations of incident irradiance can vary by 9-18%, upwelling radiance just above the sea surface by 8-18%, and remote-sensing reflectance by 12-24%. Variations in radiometric determinations can be attributed to a variety of environmental factors such as Sun angle, cloud cover, wind speed, and viewing geometry; however, wind speed is isolated as the major source of uncertainty. The above-water approach to estimating water-leaving radiance and remote-sensing reflectance is highly influenced by environmental factors. A model of the role of wind on the reflected sky radiance measured by an above-water sensor illustrates that, for clear-sky conditions and wind speeds greater than 5 m/s, determinations of water-leaving radiance at 490 nm are undercorrected by as much as 60%. A data merging procedure is presented to provide sky radiance correction parameters for above-water remote-sensing reflectance estimates. The merging results are consistent with statistical and model findings and highlight the importance of multiple field measurements in developing quality coastal oceanographic data sets for satellite ocean color algorithm development and validation.

Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost

PubMed Central

Zhou, Zhen; Huang, Jingfeng; Wang, Jing; Zhang, Kangyu; Kuang, Zhaomin; Zhong, Shiquan; Song, Xiaodong

2015-01-01

Most areas planted with sugarcane are located in southern China. However, remote sensing of sugarcane has been limited because useable remote sensing data are limited due to the cloudy climate of this region during the growing season and severe spectral mixing with other crops. In this study, we developed a methodology for automatically mapping sugarcane over large areas using time-series middle-resolution remote sensing data. For this purpose, two major techniques were used, the object-oriented method (OOM) and data mining (DM). In addition, time-series Chinese HJ-1 CCD images were obtained during the sugarcane growing period. Image objects were generated using a multi-resolution segmentation algorithm, and DM was implemented using the AdaBoost algorithm, which generated the prediction model. The prediction model was applied to the HJ-1 CCD time-series image objects, and then a map of the sugarcane planting area was produced. The classification accuracy was evaluated using independent field survey sampling points. The confusion matrix analysis showed that the overall classification accuracy reached 93.6% and that the Kappa coefficient was 0.85. Thus, the results showed that this method is feasible, efficient, and applicable for extrapolating the classification of other crops in large areas where the application of high-resolution remote sensing data is impractical due to financial considerations or because qualified images are limited. PMID:26528811

Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost.

PubMed

Zhou, Zhen; Huang, Jingfeng; Wang, Jing; Zhang, Kangyu; Kuang, Zhaomin; Zhong, Shiquan; Song, Xiaodong

2015-01-01

Most areas planted with sugarcane are located in southern China. However, remote sensing of sugarcane has been limited because useable remote sensing data are limited due to the cloudy climate of this region during the growing season and severe spectral mixing with other crops. In this study, we developed a methodology for automatically mapping sugarcane over large areas using time-series middle-resolution remote sensing data. For this purpose, two major techniques were used, the object-oriented method (OOM) and data mining (DM). In addition, time-series Chinese HJ-1 CCD images were obtained during the sugarcane growing period. Image objects were generated using a multi-resolution segmentation algorithm, and DM was implemented using the AdaBoost algorithm, which generated the prediction model. The prediction model was applied to the HJ-1 CCD time-series image objects, and then a map of the sugarcane planting area was produced. The classification accuracy was evaluated using independent field survey sampling points. The confusion matrix analysis showed that the overall classification accuracy reached 93.6% and that the Kappa coefficient was 0.85. Thus, the results showed that this method is feasible, efficient, and applicable for extrapolating the classification of other crops in large areas where the application of high-resolution remote sensing data is impractical due to financial considerations or because qualified images are limited.

Fiber-optic apparatus and method for measurement of luminescence and Raman scattering

DOEpatents

Myrick, M.L.; Angel, S.M.

1993-03-16

A dual fiber forward scattering optrode for Raman spectroscopy with the remote ends of the fibers in opposed, spaced relationship to each other to form a analyte sampling space therebetween and the method of measuring Raman spectra utilizing same are described. One optical fiber is for sending an exciting signal to the remote sampling space and, at its remote end, has a collimating microlens and an optical filter for filtering out background emissions generated in the fiber. The other optical fiber is for collecting the Raman scattering signal at the remote sampling space and, at its remote end, has a collimating microlens and an optical filter to prevent the exciting signal from the exciting fiber from entering the collection fiber and to thereby prevent the generation of background emissions in the collecting fiber.

Contactless remote induction of shear waves in soft tissues using a transcranial magnetic stimulation device

NASA Astrophysics Data System (ADS)

Grasland-Mongrain, Pol; Miller-Jolicoeur, Erika; Tang, An; Catheline, Stefan; Cloutier, Guy

2016-03-01

This study presents the first observation of shear waves induced remotely within soft tissues. It was performed through the combination of a transcranial magnetic stimulation device and a permanent magnet. A physical model based on Maxwell and Navier equations was developed. Experiments were performed on a cryogel phantom and a chicken breast sample. Using an ultrafast ultrasound scanner, shear waves of respective amplitudes of 5 and 0.5 μm were observed. Experimental and numerical results were in good agreement. This study constitutes the framework of an alternative shear wave elastography method.

Implications of alternative field-sampling designs on Landsat-based mapping of stand age and carbon stocks in Oregon forests

Treesearch

Maureen V. Duane; Warren B. Cohen; John L. Campbell; Tara Hudiburg; David P. Turner; Dale Weyermann

2010-01-01

Empirical models relating forest attributes to remotely sensed metrics are widespread in the literature and underpin many of our efforts to map forest structure across complex landscapes. In this study we compared empirical models relating Landsat reflectance to forest age across Oregon using two alternate sets of ground data: one from a large (n ~ 1500) systematic...

[Fluorescence peak shift corresponding to high chlorophyll concentrations in inland water].

PubMed

Duan, Hong-Tao; Ma, Rong-Hua; Zhang, Yuan-Zhi; Zhang, Bai

2009-01-01

Hyperspectral remote sensing offers the potential to detect water quality variables such as Chl-a by using narrow spectral channels of less than 10 nm, which could otherwise be masked by broadband satellites such as Landsat TM. Fluorescence peak of the red region is very important for the remote sensing of inland and coastal waters, which is unique to phytoplankton Chl-a that takes place in this region. Based on in situ water sampling and field spectral measurement from 2004 to 2006 in Nanhu Lake, the features of the spectral reflectance were analyzed in detail with peak position shift. The results showed: An exponential fitting model, peak position = a(Chl-a)b, was developed between chlorophyll-a concentration and fluorescence peak shift, where a varies between 686.11 and 686.29, while b between 0.0062 and 0.0065. It was found that the better the spectral resolution, the higher the precision of the model. Except that, the average of peak shift showed a high correlation with the average of different Chl-a grades, and the determination coefficient (R2) was higher than 0.81. It contributed significantly to the increase in the accuracy of the derivation of chlorophyll values from remote sensing data in Nanhu Lake. There is satisfactory correspondence between hyperspectral models and chl-a concentration, therefore, it is possible to monitor the water quality of Nanhu lake throngh the hyperspetral remote sensing data.

On the modeling of hyperspectral remote-sensing reflectance of high-sediment-load waters in the visible to shortwave-infrared domain.

PubMed

Lee, Zhongping; Shang, Shaoling; Lin, Gong; Chen, Jun; Doxaran, David

2016-03-01

We evaluated three key components in modeling hyperspectral remote-sensing reflectance in the visible to shortwave-infrared (Vis-SWIR) domain of high-sediment-load (HSL) waters, which are the relationship between remote-sensing reflectance (R(rs)) and inherent optical properties (IOPs), the absorption coefficient spectrum of pure water (a(w)) in the IR-SWIR region, and the spectral variation of sediment absorption coefficient (a(sed)). Results from this study indicate that it is necessary to use a more generalized R(rs)-IOP model to describe the spectral variation of R(rs) of HSL waters from Vis to SWIR; otherwise it may result in a spectrally distorted R(rs) spectrum if a constant model parameter is used. For hyperspectral a(w) in the IR-SWIR domain, the values reported in Kou et al. (1993) provided a much better match with the spectral variation of R(rs) in this spectral range compared to that of Segelstein (1981). For a(sed) spectrum, an empirical a(sed) spectral shape derived from sample measurements is found working much better than the traditional exponential-decay function of wavelength in modeling the spectral variation of R(rs) in the visible domain. These results would improve our understanding of the spectral signatures of R(rs) of HSL waters in the Vis-SWIR domain and subsequently improve the retrieval of IOPs from ocean color remote sensing, which could further help the estimation of sediment loading of such waters. Limitations in estimating chlorophyll concentration in such waters are also discussed.

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models.

PubMed

Forkuor, Gerald; Hounkpatin, Ozias K L; Welp, Gerhard; Thiel, Michael

2017-01-01

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties-sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen-in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models-multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)-were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources.

Workshop on New Views of the Moon: Integrated Remotely Sensed, Geophysical, and Sample Datasets

NASA Technical Reports Server (NTRS)

Jolliff, Brad L. (Editor); Ryder, Graham (Editor)

1998-01-01

It has been more than 25 years since Apollo 17 returned the last of the Apollo lunar samples. Since then, a vast amount of data has been obtained from the study of rocks and soils from the Apollo and Luna sample collections and, more recently, on a set of about a dozen lunar meteorites collected on Earth. Based on direct studies of the samples, many constraints have been established for the age, early differentiation, crust and mantle structure, and subsequent impact modification of the Moon. In addition, geophysical experiments at the surface, as well as remote sensing from orbit and Earth-based telescopic studies, have provided additional datasets about the Moon that constrain the nature of its surface and internal structure. Some might be tempted to say that we know all there is to know about the Moon and that it is time to move on from this simple satellite to more complex objects. However, the ongoing Lunar Prospector mission and the highly successful Clementine mission have provided important clues to the real geological complexity of the Moon, and have shown us that we still do not yet adequately understand the geologic history of Earth's companion. These missions, like Galileo during its lunar flyby, are providing global information viewed through new kinds of windows, and providing a fresh context for models of lunar origin, evolution, and resources, and perhaps even some grist for new questions and new hypotheses. The probable detection and characterization of water ice at the poles, the extreme concentration of Th and other radioactive elements in the Procellarum-Imbrium-Frigon's resurfaced areas of the nearside of the Moon, and the high-resolution gravity modeling enabled by these missions are examples of the kinds of exciting new results that must be integrated with the extant body of knowledge based on sample studies, in situ experiments, and remote-sensing missions to bring about the best possible understanding of the Moon and its history.

Self-referencing remote optical probe

DOEpatents

O'Rourke, Patrick E.; Prather, William S.; Livingston, Ronald R.

1991-01-01

A probe for remote spectrometric measurements of fluid samples having a hollow probe body with a sliding reflective plug therein and a lens at one end, ports for admitting and expelling the fluid sample and a means for moving the reflector so that reference measurement can be made with the reflector in a first position near the lens and a sample measurement can be made with the reflector away from the lens and the fluid sample between the reflector and the lens. Comparison of the two measurements will yield the composition of the fluid sample. The probe is preferably used for remote measurements and light is carried to and from the probe via fiber optic cables.

Self-referencing remote optical probe

DOEpatents

O'Rourke, P.E.; Prather, W.S.; Livingston, R.R.

1991-08-13

A probe is described for remote spectrometric measurements of fluid samples having a hollow probe body with a sliding reflective plug therein and a lens at one end, ports for admitting and expelling the fluid sample and a means for moving the reflector so that reference measurement can be made with the reflector in a first position near the lens and a sample measurement can be made with the reflector away from the lens and the fluid sample between the reflector and the lens. Comparison of the two measurements will yield the composition of the fluid sample. The probe is preferably used for remote measurements and light is carried to and from the probe via fiber optic cables. 3 figures.

Antibiotic-resistant Escherichia coli in migratory birds inhabiting remote Alaska

USGS Publications Warehouse

Ramey, Andy M.; Hernandez, Jorge; Tyrlöv, Veronica; Uher-Koch, Brian D.; Schmutz, Joel A.; Atterby, Clara; Järhult, Josef D.; Bonnedahl, Jonas

2018-01-01

We explored the abundance of antibiotic-resistant Escherichia coli among migratory birds at remote sites in Alaska and used a comparative approach to speculate on plausible explanations for differences in detection among species. At a remote island site, we detected antibiotic-resistant E. coli phenotypes in samples collected from glaucous-winged gulls (Larus glaucescens), a species often associated with foraging at landfills, but not in samples collected from black-legged kittiwakes (Rissa tridactyla), a more pelagic gull that typically inhabits remote areas year-round. We did not find evidence for antibiotic-resistant E. coli among 347 samples collected primarily from waterfowl at a second remote site in western Alaska. Our results provide evidence that glaucous-winged gulls may be more likely to be infected with antibiotic-resistant E. coli at remote breeding sites as compared to sympatric black-legged kittiwakes. This could be a function of the tendency of glaucous-winged gulls to forage at landfills where antibiotic-resistant bacterial infections may be acquired and subsequently dispersed. The low overall detection of antibiotic-resistant E. coli in migratory birds sampled at remote sites in Alaska is consistent with the premise that anthropogenic inputs into the local environment or the relative lack thereof influences the prevalence of antibiotic-resistant bacteria among birds inhabiting the area.

A Comparison of Multivariate and Pre-Processing Methods for Quantitative Laser-Induced Breakdown Spectroscopy of Geologic Samples

NASA Technical Reports Server (NTRS)

Anderson, R. B.; Morris, R. V.; Clegg, S. M.; Bell, J. F., III; Humphries, S. D.; Wiens, R. C.

2011-01-01

The ChemCam instrument selected for the Curiosity rover is capable of remote laser-induced breakdown spectroscopy (LIBS).[1] We used a remote LIBS instrument similar to ChemCam to analyze 197 geologic slab samples and 32 pressed-powder geostandards. The slab samples are well-characterized and have been used to validate the calibration of previous instruments on Mars missions, including CRISM [2], OMEGA [3], the MER Pancam [4], Mini-TES [5], and Moessbauer [6] instruments and the Phoenix SSI [7]. The resulting dataset was used to compare multivariate methods for quantitative LIBS and to determine the effect of grain size on calculations. Three multivariate methods - partial least squares (PLS), multilayer perceptron artificial neural networks (MLP ANNs) and cascade correlation (CC) ANNs - were used to generate models and extract the quantitative composition of unknown samples. PLS can be used to predict one element (PLS1) or multiple elements (PLS2) at a time, as can the neural network methods. Although MLP and CC ANNs were successful in some cases, PLS generally produced the most accurate and precise results.

[Mapping environmental vulnerability from ETM + data in the Yellow River Mouth Area].

PubMed

Wang, Rui-Yan; Yu, Zhen-Wen; Xia, Yan-Ling; Wang, Xiang-Feng; Zhao, Geng-Xing; Jiang, Shu-Qian

2013-10-01

The environmental vulnerability retrieval is important to support continuing data. The spatial distribution of regional environmental vulnerability was got through remote sensing retrieval. In view of soil and vegetation, the environmental vulnerability evaluation index system was built, and the environmental vulnerability of sampling points was calculated by the AHP-fuzzy method, then the correlation between the sampling points environmental vulnerability and ETM + spectral reflectance ratio including some kinds of conversion data was analyzed to determine the sensitive spectral parameters. Based on that, models of correlation analysis, traditional regression, BP neural network and support vector regression were taken to explain the quantitative relationship between the spectral reflectance and the environmental vulnerability. With this model, the environmental vulnerability distribution was retrieved in the Yellow River Mouth Area. The results showed that the correlation between the environmental vulnerability and the spring NDVI, the September NDVI and the spring brightness was better than others, so they were selected as the sensitive spectral parameters. The model precision result showed that in addition to the support vector model, the other model reached the significant level. While all the multi-variable regression was better than all one-variable regression, and the model accuracy of BP neural network was the best. This study will serve as a reliable theoretical reference for the large spatial scale environmental vulnerability estimation based on remote sensing data.

A model for a drug distribution system in remote Australia as a social determinant of health using event structure analysis.

PubMed

Rovers, John P; Mages, Michelle D

2017-09-25

The social determinants of health include the health systems under which people live and utilize health services. One social determinant, for which pharmacists are responsible, is designing drug distribution systems that ensure patients have safe and convenient access to medications. This is critical for settings with poor access to health care. Rural and remote Australia is one example of a setting where the pharmacy profession, schools of pharmacy, and regulatory agencies require pharmacists to assure medication access. Studies of drug distribution systems in such settings are uncommon. This study describes a model for a drug distribution system in an Aboriginal Health Service in remote Australia. The results may be useful for policy setting, pharmacy system design, health professions education, benchmarking, or quality assurance efforts for health system managers in similarly remote locations. The results also suggest that pharmacists can promote access to medications as a social determinant of health. The primary objective of this study was to propose a model for a drug procurement, storage, and distribution system in a remote region of Australia. The secondary objective was to learn the opinions and experiences of healthcare workers under the model. Qualitative research methods were used. Semi-structured interviews were performed with a convenience sample of 11 individuals employed by an Aboriginal health service. Transcripts were analyzed using Event Structure Analysis (ESA) to develop the model. Transcripts were also analyzed to determine the opinions and experiences of health care workers. The model was comprised of 24 unique steps with seven distinct components: choosing a supplier; creating a list of preferred medications; budgeting and ordering; supply and shipping; receipt and storage in the clinic; prescribing process; dispensing and patient counseling. Interviewees described opportunities for quality improvement in choosing suppliers, legal issues and staffing, cold chain integrity, medication shortages and wastage, and adherence to policies. The model illustrates how pharmacists address medication access as a social determinant of health, and may be helpful for policy setting, system design, benchmarking, and quality assurance by health system designers. ESA is an effective and novel method of developing such models.

Research on assessment and improvement method of remote sensing image reconstruction

NASA Astrophysics Data System (ADS)

Sun, Li; Hua, Nian; Yu, Yanbo; Zhao, Zhanping

2018-01-01

Remote sensing image quality assessment and improvement is an important part of image processing. Generally, the use of compressive sampling theory in remote sensing imaging system can compress images while sampling which can improve efficiency. A method of two-dimensional principal component analysis (2DPCA) is proposed to reconstruct the remote sensing image to improve the quality of the compressed image in this paper, which contain the useful information of image and can restrain the noise. Then, remote sensing image quality influence factors are analyzed, and the evaluation parameters for quantitative evaluation are introduced. On this basis, the quality of the reconstructed images is evaluated and the different factors influence on the reconstruction is analyzed, providing meaningful referential data for enhancing the quality of remote sensing images. The experiment results show that evaluation results fit human visual feature, and the method proposed have good application value in the field of remote sensing image processing.

Mercury deposition in snow near an industrial emission source in the western U.S. and comparison to ISC3 model predictions

USGS Publications Warehouse

Abbott, M.L.; Susong, D.D.; Krabbenhoft, D.P.; Rood, A.S.

2002-01-01

Mercury (total and methyl) was evaluated in snow samples collected near a major mercury emission source on the Idaho National Engineering and Environmental Laboratory (INEEL) in southeastern Idaho and 160 km downwind in Teton Range in western Wyoming. The sampling was done to assess near-field (<12 km) deposition rates around the source, compare them to those measured in a relatively remote, pristine downwind location, and to use the measurements to develop improved, site-specific model input parameters for precipitation scavenging coefficient and the fraction of Hg emissions deposited locally. Measured snow water concentrations (ng L-1) were converted to deposition (ug m-2) using the sample location snow water equivalent. The deposition was then compared to that predicted using the ISC3 air dispersion/deposition model which was run with a range of particle and vapor scavenging coefficient input values. Accepted model statistical performance measures (fractional bias and normalized mean square error) were calculated for the different modeling runs, and the best model performance was selected. Measured concentrations close to the source (average = 5.3 ng L-1) were about twice those measured in the Teton Range (average = 2.7 ng L-1) which were within the expected range of values for remote background areas. For most of the sampling locations, the ISC3 model predicted within a factor of two of the observed deposition. The best modeling performance was obtained using a scavenging coefficient value for 0.25 ??m diameter particulate and the assumption that all of the mercury is reactive Hg(II) and subject to local deposition. A 0.1 ??m particle assumption provided conservative overprediction of the data, while a vapor assumption resulted in highly variable predictions. Partitioning a fraction of the Hg emissions to elemental Hg(0) (a U.S. EPA default assumption for combustion facility risk assessments) would have underpredicted the observed fallout.

The micron- to kilometer-scale Moon: linking samples to orbital observations, Apollo to LRO

NASA Astrophysics Data System (ADS)

Crites, S.; Lucey, P. G.; Taylor, J.; Martel, L.; Sun, L.; Honniball, C.; Lemelin, M.

2017-12-01

The Apollo missions have shaped the field of lunar science and our understanding of the Moon, from global-scale revelations like the magma ocean hypothesis, to providing ground truth for compositional remote sensing and absolute ages to anchor cratering chronologies. While lunar meteorite samples can provide a global- to regional-level view of the Moon, samples returned from known locations are needed to directly link orbital-scale observations with laboratory measurements-a link that can be brought to full fruition with today's extremely high spatial resolution observations from Lunar Reconnaissance Orbiter and other recent missions. Korotev et al. (2005) described a scenario of the Moon without Apollo to speculate about our understanding of the Moon if our data were confined to lunar meteorites and remote sensing. I will review some of the major points discussed by Korotev et al. (2005), and focus on some of the ways in which spectroscopic remote sensing in particular has benefited from the Apollo samples. For example, could the causes and effects of lunar-style space weathering have been unraveled without the Apollo samples? What would be the limitations on remote sensing compositional measurements that rely on Apollo samples for calibration and validation? And what new opportunities to bring together orbital and sample analyses now exist, in light of today's high spatial and spectral resolution remote sensing datasets?

Remote analysis of anthropogenic effect on boreal forests using nonlinear multidimensional models

NASA Astrophysics Data System (ADS)

Shchemel, Anton; Ivanova, Yuliya; Larko, Alexander

Nowadays anthropogenic stress of mining and refining oil and gas is becoming significant prob-lem in Eastern Siberia. The task of revealing effect of that industry is not trivial because of complicated access to the sites of mining. Due to that, severe problem of supplying detection of oil and gas complex effect on forest ecosystems arises. That estimation should allow revealing the sites of any negative changes in forest communities in proper time. The intellectual system of analyzing remote sensing data of different resolution and different spectral characteristics with sophisticated nonlinear models is dedicated to solve the problem. The work considers re-mote detection and estimation of forest degradation using analysis of free remote sensing data without total field observations of oil and gas mining territory. To analyze a state of vegetation the following remote sensing data were used as input parameters for our models: albedo, surface temperature and data of about thirty spectral bands in visible and infrared region. The data of MODIS satellite from the year 2000 was used. Chosen data allowed producing complex estima-tion of parameters linked with the quality (set of species, physiological state) and the quantity of vegetation. To verify obtained estimation each index was calculated for a territory in which oil and gas mining is provided along with the same calculations for a sample "clear" territory. Monthly data for vegetation period and annual mean values were analyzed. The work revealed some trends of annual data probably linked with intensification of anthropogenic effect on the ecosystems. The models we managed to build are easy to apply for using by fair personnel of emergency control and oversight institutions. It was found to be helpful to use exactly the full set of values obtained from the satellite for multilateral estimation of anthropogenic effect on forest ecosystems of objects of the oil mining industry for producing generalized estimation indices by the developed models.

Studies in remote sensing of Southern California and related environments

NASA Technical Reports Server (NTRS)

Bowden, L. W.

1971-01-01

A summary is presented of the research activities in southern California to determine whether meaningful geographic information was obtainable by use of remote sensing in an area already well documented or if the techniques and methodology could be transferred to related environments. Several broad characteristics of the regional geography were investigated with regards to their feasibility to be studied by aircraft and spacecraft sensors to improve the inventory and understanding of resources and environmental circumstances and to serve as models for future geographic analysis of other regions when using remote sensing devices. Sample activities are described in detail and three experiments producing worthwhile results are highlighted: mapping montane vegetation with color IR imagery, analysis of urban residual environment using color IR aerial photography, and regional agricultural land use mapping tested against color IR photography.

A random optimization approach for inherent optic properties of nearshore waters

NASA Astrophysics Data System (ADS)

Zhou, Aijun; Hao, Yongshuai; Xu, Kuo; Zhou, Heng

2016-10-01

Traditional method of water quality sampling is time-consuming and highly cost. It can not meet the needs of social development. Hyperspectral remote sensing technology has well time resolution, spatial coverage and more general segment information on spectrum. It has a good potential in water quality supervision. Via the method of semi-analytical, remote sensing information can be related with the water quality. The inherent optical properties are used to quantify the water quality, and an optical model inside the water is established to analysis the features of water. By stochastic optimization algorithm Threshold Acceptance, a global optimization of the unknown model parameters can be determined to obtain the distribution of chlorophyll, organic solution and suspended particles in water. Via the improvement of the optimization algorithm in the search step, the processing time will be obviously reduced, and it will create more opportunity for the increasing the number of parameter. For the innovation definition of the optimization steps and standard, the whole inversion process become more targeted, thus improving the accuracy of inversion. According to the application result for simulated data given by IOCCG and field date provided by NASA, the approach model get continuous improvement and enhancement. Finally, a low-cost, effective retrieval model of water quality from hyper-spectral remote sensing can be achieved.

Assimilation of remote sensing data into a process-based ecosystem model for monitoring changes of soil water content in croplands

NASA Astrophysics Data System (ADS)

Ju, Weimin; Gao, Ping; Wang, Jun; Li, Xianfeng; Chen, Shu

2008-10-01

Soil water content (SWC) is an important factor affecting photosynthesis, growth, and final yields of crops. The information on SWC is of importance for mitigating the reduction of crop yields caused by drought through proper agricultural water management. A variety of methodologies have been developed to estimate SWC at local and regional scales, including field sampling, remote sensing monitoring and model simulations. The reliability of regional SWC simulation depends largely on the accuracy of spatial input datasets, including vegetation parameters, soil and meteorological data. Remote sensing has been proved to be an effective technique for controlling uncertainties in vegetation parameters. In this study, the vegetation parameters (leaf area index and land cover type) derived from the Moderate Resolution Imaging Spectrometer (MODIS) were assimilated into a process-based ecosystem model BEPS for simulating the variations of SWC in croplands of Jiangsu province, China. Validation shows that the BEPS model is able to capture 81% and 83% of across-site variations of SWC at 10 and 20 cm depths during the period from September to December, 2006 when a serous autumn drought occurred. The simulated SWC responded the events of rainfall well at regional scale, demonstrating the usefulness of our methodology for SWC and practical agricultural water management at large scales.

Predicting risk of invasive species occurrence - remote-sesning strategies

USDA-ARS?s Scientific Manuscript database

Remote sensing is a means to describe characteristics of an area without physically sampling the area. Remote sensors can be mounted on a satellite, plane, or other airborne structure. Remotely sensed data allow for landscape perspectives on management issues. Sensors measure the electromagnetic ene...

Implications of sampling design and sample size for national carbon accounting systems.

PubMed

Köhl, Michael; Lister, Andrew; Scott, Charles T; Baldauf, Thomas; Plugge, Daniel

2011-11-08

Countries willing to adopt a REDD regime need to establish a national Measurement, Reporting and Verification (MRV) system that provides information on forest carbon stocks and carbon stock changes. Due to the extensive areas covered by forests the information is generally obtained by sample based surveys. Most operational sampling approaches utilize a combination of earth-observation data and in-situ field assessments as data sources. We compared the cost-efficiency of four different sampling design alternatives (simple random sampling, regression estimators, stratified sampling, 2-phase sampling with regression estimators) that have been proposed in the scope of REDD. Three of the design alternatives provide for a combination of in-situ and earth-observation data. Under different settings of remote sensing coverage, cost per field plot, cost of remote sensing imagery, correlation between attributes quantified in remote sensing and field data, as well as population variability and the percent standard error over total survey cost was calculated. The cost-efficiency of forest carbon stock assessments is driven by the sampling design chosen. Our results indicate that the cost of remote sensing imagery is decisive for the cost-efficiency of a sampling design. The variability of the sample population impairs cost-efficiency, but does not reverse the pattern of cost-efficiency of the individual design alternatives. Our results clearly indicate that it is important to consider cost-efficiency in the development of forest carbon stock assessments and the selection of remote sensing techniques. The development of MRV-systems for REDD need to be based on a sound optimization process that compares different data sources and sampling designs with respect to their cost-efficiency. This helps to reduce the uncertainties related with the quantification of carbon stocks and to increase the financial benefits from adopting a REDD regime.

Drought Management Activities of the National Drought Mitigation Center (NDMC): Contributions Toward a Global Drought Early Warning System (GDEWS)

NASA Astrophysics Data System (ADS)

Stumpf, A.; Lachiche, N.; Malet, J.; Kerle, N.; Puissant, A.

2011-12-01

VHR satellite images have become a primary source for landslide inventory mapping after major triggering events such as earthquakes and heavy rainfalls. Visual image interpretation is still the prevailing standard method for operational purposes but is time-consuming and not well suited to fully exploit the increasingly better supply of remote sensing data. Recent studies have addressed the development of more automated image analysis workflows for landslide inventory mapping. In particular object-oriented approaches that account for spatial and textural image information have been demonstrated to be more adequate than pixel-based classification but manually elaborated rule-based classifiers are difficult to adapt under changing scene characteristics. Machine learning algorithm allow learning classification rules for complex image patterns from labelled examples and can be adapted straightforwardly with available training data. In order to reduce the amount of costly training data active learning (AL) has evolved as a key concept to guide the sampling for many applications. The underlying idea of AL is to initialize a machine learning model with a small training set, and to subsequently exploit the model state and data structure to iteratively select the most valuable samples that should be labelled by the user. With relatively few queries and labelled samples, an AL strategy yields higher accuracies than an equivalent classifier trained with many randomly selected samples. This study addressed the development of an AL method for landslide mapping from VHR remote sensing images with special consideration of the spatial distribution of the samples. Our approach [1] is based on the Random Forest algorithm and considers the classifier uncertainty as well as the variance of potential sampling regions to guide the user towards the most valuable sampling areas. The algorithm explicitly searches for compact regions and thereby avoids a spatially disperse sampling pattern inherent to most other AL methods. The accuracy, the sampling time and the computational runtime of the algorithm were evaluated on multiple satellite images capturing recent large scale landslide events. Sampling between 1-4% of the study areas the accuracies between 74% and 80% were achieved, whereas standard sampling schemes yielded only accuracies between 28% and 50% with equal sampling costs. Compared to commonly used point-wise AL algorithm the proposed approach significantly reduces the number of iterations and hence the computational runtime. Since the user can focus on relatively few compact areas (rather than on hundreds of distributed points) the overall labeling time is reduced by more than 50% compared to point-wise queries. An experimental evaluation of multiple expert mappings demonstrated strong relationships between the uncertainties of the experts and the machine learning model. It revealed that the achieved accuracies are within the range of the inter-expert disagreement and that it will be indispensable to consider ground truth uncertainties to truly achieve further enhancements in the future. The proposed method is generally applicable to a wide range of optical satellite images and landslide types. [1] A. Stumpf, N. Lachiche, J.-P. Malet, N. Kerle, and A. Puissant, Active learning in the spatial domain for remote sensing image classification, IEEE Transactions on Geosciece and Remote Sensing. 2013, DOI 10.1109/TGRS.2013.2262052.

Remote and Ground Truth Spectral Measurement Comparisons of FORMOSAT III

NASA Technical Reports Server (NTRS)

Abercromby, Kira Jorgensen; Hamada, Kris; Guyote, Michael; Okada, Jennifer; Barker, Edwin

2007-01-01

FORMOSAT III are a set of six research satellites from Taiwan that were launched in April 2006. The satellites are in 800 km, 71 degree inclination orbits and separated by 24 degrees in ascending node. Laboratory spectral measurements were taken of outer surface materials on FORMOSAT III. From those measurements, a computer model was built to predict the spectral reflectance accounting for both solar phase angle and orientation of the spacecraft relative to the observer. However, materials exposed to the space environment have exhibited spectral changes including a darkening and a "reddening" of the spectra. This "reddening" is characterized by an increase in slope of the reflectance as the wavelength increases. Therefore, the model of pre-flight materials was augmented to include the presumed causative agent: space weathering effects. Remote data were collected on two of the six FORMOSAT satellites using the 1.6 meter telescope at the AMOS (Air Force Maui Optical and Supercomputing) site with the Spica spectrometer. Due to the separation in ascending node, observations were acquired of whichever one of the six satellites was visible on that specific night. Three nights of data were collected using the red (6000 - 9500 angstroms) filter and five nights of data were collected using the blue (3200 - 6600 angstroms) filter. A comparison of the data showed a good match to the pre-flight models for the blue filter region. The absorption feature near 5500 angstroms due to the copper colored Kapton multi-layer insulation (MLI) was very apparent in the remote samples and a good fit to the data was seen in all satellites observed. The features in the red filter regime agreed with the pre-flight model up through 7000 angstroms where the reddening begins and the slope of the remote sample increases. A comparison of the satellites showed similar features in the red and blue filter regions, i.e. the satellite surfaces were aging at the same rate. A comparison of the pre-flight model to the first month of remote measurements showed the amount by which the satellite had reddened. The second month of data observed a satellite at a higher altitude and was therefore, not compared to the first month. A third month of data was collected but of satellites at the lower altitude regime and can only be compared to the first month. One cause of the reddening that was ruled out in early papers was a possible calibration issue.

Thermal conductivity of lunar regolith simulant JSC-1A under vacuum

NASA Astrophysics Data System (ADS)

Sakatani, Naoya; Ogawa, Kazunori; Arakawa, Masahiko; Tanaka, Satoshi

2018-07-01

Many air-less planetary bodies, including the Moon, asteroids, and comets, are covered by regolith. The thermal conductivity of the regolith is an essential parameter controlling the surface temperature variation. A thermal conductivity model applicable to natural soils as well as planetary surface regolith is required to analyze infrared remote sensing data. In this study, we investigated the temperature and compressional stress dependence of the thermal conductivity of the lunar regolith simulant JSC-1A, and the temperature dependence of sieved JSC-1A samples under vacuum conditions. We confirmed that a series of the experimental data for JSC-1A are fitted well by our analytical model of the thermal conductivity (Sakatani et al., 2017). Comparison with the calibration data of the sieved samples with those for original JSC-1A indicates that the thermal conductivity of natural samples with a wide grain size distribution can be modeled as mono-sized grains with a volumetric median size. The calibrated model can be used to estimate the volumetric median grain size from infrared remote sensing data. Our experiments and the calibrated model indicates that uncompressed JSC-1A has similar thermal conductivity to lunar top-surface materials, but the lunar subsurface thermal conductivity cannot be explained only by the effects of the density and self-weighted compressional stress. We infer that the nature of the lunar subsurface regolith grains is much different from JSC-1A and lunar top-surface regolith, and/or the lunar subsurface regolith is over-consolidated and the compressional stress higher than the hydrostatic pressure is stored in the lunar regolith layer.

Specific absorption and backscatter coefficient signatures in southeastern Atlantic coastal waters

NASA Astrophysics Data System (ADS)

Bostater, Charles R., Jr.

1998-12-01

Measurements of natural water samples in the field and laboratory of hyperspectral signatures of total absorption and reflectance were obtained using long pathlength absorption systems (50 cm pathlength). Water was sampled in Indian River Lagoon, Banana River and Port Canaveral, Florida. Stations were also occupied in near coastal waters out to the edge of the Gulf Stream in the vicinity of Kennedy Space Center, Florida and estuarine waters along Port Royal Sound and along the Beaufort River tidal area in South Carolina. The measurements were utilized to calculate natural water specific absorption, total backscatter and specific backscatter optical signatures. The resulting optical cross section signatures suggest different models are needed for the different water types and that the common linear model may only appropriate for coastal and oceanic water types. Mean particle size estimates based on the optical cross section, suggest as expected, that particle size of oceanic particles are smaller than more turbid water types. The data discussed and presented are necessary for remote sensing applications of sensors as well as for development and inversion of remote sensing algorithms.

Which ecological determinants influence Australian children's fruit and vegetable consumption?

PubMed

Godrich, Stephanie L; Davies, Christina R; Darby, Jill; Devine, Amanda

2018-04-01

This study investigated determinants of fruit and vegetable (F&V) consumption among regional and remote Western Australian (WA) children, using an Ecological Model of Health Behaviour. Semi-structured interviews were conducted with 20 key informants (Health Workers, Food Supply Workers, and School/Youth Workers) purposively sampled from across regional and remote WA. Interviews were transcribed, analysed thematically using QSR-NVivo 10 software, and embedded within an Ecological Model of Health Behaviour to demonstrate the multiple levels of influence on health. Key determinants of F&V consumption at the intrapersonal level included attitude and food literacy among children. Key interpersonal level determinants included role modelling and parental food literacy. Institutional determinants included health service provision, school nutrition education and food skill programs. F&V availability, community networks and health-promoting spaces were key themes affecting families at the community level. The public policy level influencer included implementation of a store policy within local food outlets. Study findings suggested participatory programs with an emphasis on parental involvement and role modelling could increase F&V intake among children living in regional and remote areas; while school curriculum linkages were essential for school-based programs. Policy makers should consider further investment in school food literacy programs and family programs that are delivered collaboratively. Further, support of local food supply options and support for healthy food policies in food outlets are critical next steps. This study contributes new knowledge to build the evidence base and facilitate the development of targeted strategies to increase consumption of F&V among children living in regional and remote areas.

Remote sensing of Earth terrain

NASA Technical Reports Server (NTRS)

Kong, J. A.

1993-01-01

Progress report on remote sensing of Earth terrain covering the period from Jan. to June 1993 is presented. Areas of research include: radiative transfer model for active and passive remote sensing of vegetation canopy; polarimetric thermal emission from rough ocean surfaces; polarimetric passive remote sensing of ocean wind vectors; polarimetric thermal emission from periodic water surfaces; layer model with tandom spheriodal scatterers for remote sensing of vegetation canopy; application of theoretical models to active and passive remote sensing of saline ice; radiative transfer theory for polarimetric remote sensing of pine forest; scattering of electromagnetic waves from a dense medium consisting of correlated mie scatterers with size distributions and applications to dry snow; variance of phase fluctuations of waves propagating through a random medium; polarimetric signatures of a canopy of dielectric cylinders based on first and second order vector radiative transfer theory; branching model for vegetation; polarimetric passive remote sensing of periodic surfaces; composite volume and surface scattering model; and radar image classification.

The relationship between geographic remoteness and intentions to use a telephone support service among Australian men following radical prostatectomy.

PubMed

Corboy, Denise; McLaren, Suzanne; Jenkins, Megan; McDonald, John

2014-11-01

The objective is to investigate the influence of characteristics related to place of residence (self-reliance and stoicism) on men's intentions to use a telephone support service following radical prostatectomy. A community sample of 447 prostate cancer patients (31% response), recruited via Medicare Australia, completed a survey to assess levels of self-reliance and stoicism, and beliefs about addressing emotional distress through using telephone support services. Results indicated that the model was a partially mediated model. Geographic remoteness was directly related to intention, and indirectly related through stoicism and subjective norms. Men from rural and remote areas in Australia might face particular challenges in seeking support following treatment for prostate cancer. These challenges appear to relate to the influence of stoic attitudes and normative expectations, than to issues of access and availability. Addressing stoic attitudes in the clinical setting, through normalising emotional reactions to cancer diagnosis and treatment, and the act of help-seeking for emotional support, may be beneficial. Copyright © 2014 John Wiley & Sons, Ltd.

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models

PubMed Central

Welp, Gerhard; Thiel, Michael

2017-01-01

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties–sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen–in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models–multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)–were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources. PMID:28114334

Spatiotemporal Variability of Soil Hydraulic Properties from Field Data and Remote Sensing in the Walnut Gulch Experimental Watershed

NASA Astrophysics Data System (ADS)

Becker, R.; Gebremichael, M.; Marker, M.

2015-12-01

Soil moisture is one of the main input variables for hydrological models. However due to the high spatial and temporal variability of soil properties it is often difficult to obtain accurate soil information at the required resolution. The new satellite SMAP promises to deliver soil moisture information at higher resolutions and could therefore improve the results of hydrological models. Nevertheless it still has to be investigated how precisely the SMAP soil moisture data can be used to delineate rainfall-runoff generation processes and if SMAP imagery can significantly improve the results of surface runoff models. Important parameters to understand the spatiotemporal distribution of soil humidity are infiltration and hydraulic conductivities apart from soil texture and macrostructure. During the SMAPVEX15-field campaign data on hydraulic conductivity and infiltration rates is collected in the Walnut Gulch Experimental Watershed (WGEW) in Southeastern Arizona in order to analyze the spatiotemporal variability of soil hydraulic properties. A Compact Constant Head Permeameter is used for in situ measurements of saturated hydraulic conductivity within the soil layers and a Hood Infiltrometer is used to determine infiltration rates at the undisturbed soil surface. Sampling sites were adjacent to the USDA-ARS meteorological and soil moisture measuring sites in the WGEW to take advantage of the long-term database of soil and climate data. Furthermore a sample plot of 3x3km was selected, where the spatial variability of soil hydraulic properties within a SMAP footprint was investigated. The results of the ground measurement based analysis are then compared with the remote sensing data derived from SMAP and aircraft-based microwave data to determine how well these spatiotemporal variations are captured by the remotely sensed data with the final goal of evaluating the use of future satellite soil moisture products for the improvement of rainfall runoff models. The results reveal several interesting features on the spatiotemporal variability of soil moisture at multiple scales, and the capabilities and limitations of remote sensing derived products in reproducing them.

Random-Forest Classification of High-Resolution Remote Sensing Images and Ndsm Over Urban Areas

NASA Astrophysics Data System (ADS)

Sun, X. F.; Lin, X. G.

2017-09-01

As an intermediate step between raw remote sensing data and digital urban maps, remote sensing data classification has been a challenging and long-standing research problem in the community of remote sensing. In this work, an effective classification method is proposed for classifying high-resolution remote sensing data over urban areas. Starting from high resolution multi-spectral images and 3D geometry data, our method proceeds in three main stages: feature extraction, classification, and classified result refinement. First, we extract color, vegetation index and texture features from the multi-spectral image and compute the height, elevation texture and differential morphological profile (DMP) features from the 3D geometry data. Then in the classification stage, multiple random forest (RF) classifiers are trained separately, then combined to form a RF ensemble to estimate each sample's category probabilities. Finally the probabilities along with the feature importance indicator outputted by RF ensemble are used to construct a fully connected conditional random field (FCCRF) graph model, by which the classification results are refined through mean-field based statistical inference. Experiments on the ISPRS Semantic Labeling Contest dataset show that our proposed 3-stage method achieves 86.9% overall accuracy on the test data.

Remote sensing in precision farming: real-time monitoring of water and fertilizer requirements of agricultural crops

NASA Astrophysics Data System (ADS)

Zilberman, Arkadi; Ben Asher, Jiftah; Kopeika, Norman S.

2016-10-01

The advancements in remote sensing in combination with sensor technology (both passive and active) enable growers to analyze an entire crop field as well as its local features. In particular, changes of actual evapo-transpiration (ET) as a function of water availability can be measured remotely with infrared radiometers. Detection of crop water stress and ET and combining it with the soil water flow model enable rational irrigation timing and application amounts. Nutrient deficiency, and in particular nitrogen deficiency, causes substantial crop losses. This deficiency needs to be identified immediately. A faster the detection and correction, a lesser the damage to the crop yield. In the present work, to retrieve ET a novel deterministic approach was used which is based on the remote sensing data. The algorithm can automatically provide timely valuable information on plant and soil water status, which can improve the management of irrigated crops. The solution is capable of bridging between Penman-Monteith ET model and Richards soil water flow model. This bridging can serve as a preliminary tool for expert irrigation system. To support decisions regarding fertilizers the greenness of plant canopies is assessed and quantified by using the spectral reflectance sensors and digital color imaging. Fertilization management can be provided on the basis of sampling and monitoring of crop nitrogen conditions using RS technique and translating measured N concentration in crop to kg/ha N application in the field.

Quantifying ecosystem carbon losses and gains following development in New England: A combined field, modeling, and remote sensing approach

NASA Astrophysics Data System (ADS)

Raciti, S. M.; Hutyra, L.; Briber, B. M.; Dunn, A. L.; Friedl, M. A.; Woodcock, C.; Zhu, Z.; Olofsson, P.

2013-12-01

If current trends continue, the world's urban population may double and urban land area may quadruple over the next 50 years. Despite the rapid expansion of urban areas, the trajectories of carbon losses and gains following development remain poorly quantified. We are using a combination of field measurements, modeling, and remote sensing to advance our ability to measure and monitor trajectories of ecosystem carbon over space and time. To characterize how carbon stocks change across urban-to-rural gradients, we previously established field plots to survey live and dead tree biomass, tree canopy, soil and foliar carbon and nitrogen concentrations, and a range of landscape characteristics (Raciti et al. 2012). In 2013, we extended our field sampling to focus specifically on places that experienced land use and land cover change over the past 35 years. This chronosequence approach was informed by Landsat time series (1982-present) and property records (before 1982). The Landsat time series approach differs from traditional remote-sensing-based land use change detection methods because it leverages the entire Landsat archive of imagery using a Fourier fitting approach (Zhu et al. 2012). The result is a temporally and spatially continuous map of land use and land cover change across the study region. We used these field and remote sensing data to inform a carbon bookkeeping model that estimates changes in past and potential future carbon stocks over time. Here we present preliminary results of this work for eastern Massachusetts.

A Novel approach to monitor chlorophyll-a concentration using an adaptive model from MODIS data at 250 metres spatial resolution

NASA Astrophysics Data System (ADS)

El Alem, A.; Chokmani, K.; Laurion, I.; El Adlouni, S.

2013-12-01

Occurrence and extent of Harmful Algal Bloom (HAB) has increased in inland water bodies around the world. The appearance of these blooms reflects the advanced state of eutrophication of several aquatic systems caused by urban, agricultural, and industrial development. Algal blooms, especially those cyanobacterial origins, are capable to produce and release toxins, threatening human and animal health, quality of drinking water, and recreational water bodies. Conventional monitoring networks, based on infrequent sampling in a few fixed monitoring stations, cannot provide the information needed as HABs are spatially and temporally heterogeneous. Remote sensing represents an interesting alternative to provide the required spatial and temporal coverage. The usefulness of air-borne and satellite remote sensing data to detect HABs was demonstrated since three decades ago, and since several empirical and semi-empirical models, using satellite imagery, were developed to estimate chlorophyll-a concentration [Chl-a] as a proxy to detect bloom proliferations. However, most of those models presented several weaknesses that are generally linked to the range of [Chl-a] to be estimated. Indeed, models originally calibrated for high [Chl-a] fail to estimate low concentrations and vice versa. In this study, an adaptive model to estimate [Chl-a], spread over a wide range of concentrations, is developed for optically complex inland water bodies based on combination of water spectral response classification and three developed semi-empirical algorithms using a multivariate regression. Three distinct water types (low, medium, and high [Chl-a]) are first identified using the Classification and Regression Tree (CART) method performed on remote sensing reflectance over a dataset of 44 [Chl-a] samples collected from Lakes over Quebec province. Based on the water classification, a specific multivariate model to each water type is developed using the same dataset and the MODIS data at 250-m spatial resolution. By pre-clustering inland water bodies, the results were very interesting as the determination coefficients as well as the relative RMSE of the cross-validation were of 0.99, 0.98 and 0.95 and of 0.5%, 8% and 17% for high, medium, and low [Chl-a], respectively. On the other hand, the adaptive model reached a global success rate of 92% using an independent, semi-qualitative, [Chl-a] samples collected over more than twenty inland water bodies for the years 2009 and 2010 over the Quebec province.

Absolute and relative emissions analysis in practical combustion systems—effect of water vapor condensation

NASA Astrophysics Data System (ADS)

Richter, J. P.; Mollendorf, J. C.; DesJardin, P. E.

2016-11-01

Accurate knowledge of the absolute combustion gas composition is necessary in the automotive, aircraft, processing, heating and air conditioning industries where emissions reduction is a major concern. Those industries use a variety of sensor technologies. Many of these sensors are used to analyze the gas by pumping a sample through a system of tubes to reach a remote sensor location. An inherent characteristic with this type of sampling strategy is that the mixture state changes as the sample is drawn towards the sensor. Specifically, temperature and humidity changes can be significant, resulting in a very different gas mixture at the sensor interface compared with the in situ location (water vapor dilution effect). Consequently, the gas concentrations obtained from remotely sampled gas analyzers can be significantly different than in situ values. In this study, inherent errors associated with sampled combustion gas concentration measurements are explored, and a correction methodology is presented to determine the absolute gas composition from remotely measured gas species concentrations. For in situ (wet) measurements a heated zirconium dioxide (ZrO2) oxygen sensor (Bosch LSU 4.9) is used to measure the absolute oxygen concentration. This is used to correct the remotely sampled (dry) measurements taken with an electrochemical sensor within the remote analyzer (Testo 330-2LL). In this study, such a correction is experimentally validated for a specified concentration of carbon monoxide (5020 ppmv).

Space-Time Data fusion for Remote Sensing Applications

NASA Technical Reports Server (NTRS)

Braverman, Amy; Nguyen, H.; Cressie, N.

2011-01-01

NASA has been collecting massive amounts of remote sensing data about Earth's systems for more than a decade. Missions are selected to be complementary in quantities measured, retrieval techniques, and sampling characteristics, so these datasets are highly synergistic. To fully exploit this, a rigorous methodology for combining data with heterogeneous sampling characteristics is required. For scientific purposes, the methodology must also provide quantitative measures of uncertainty that propagate input-data uncertainty appropriately. We view this as a statistical inference problem. The true but notdirectly- observed quantities form a vector-valued field continuous in space and time. Our goal is to infer those true values or some function of them, and provide to uncertainty quantification for those inferences. We use a spatiotemporal statistical model that relates the unobserved quantities of interest at point-level to the spatially aggregated, observed data. We describe and illustrate our method using CO2 data from two NASA data sets.

Sample project: establishing a global forest monitoring capability using multi-resolution and multi-temporal remotely sensed data sets

USGS Publications Warehouse

Hansen, Matt; Stehman, Steve; Loveland, Tom; Vogelmann, Jim; Cochrane, Mark

2009-01-01

Quantifying rates of forest-cover change is important for improved carbon accounting and climate change modeling, management of forestry and agricultural resources, and biodiversity monitoring. A practical solution to examining trends in forest cover change at global scale is to employ remotely sensed data. Satellite-based monitoring of forest cover can be implemented consistently across large regions at annual and inter-annual intervals. This research extends previous research on global forest-cover dynamics and land-cover change estimation to establish a robust, operational forest monitoring and assessment system. The approach integrates both MODIS and Landsat data to provide timely biome-scale forest change estimation. This is achieved by using annual MODIS change indicator maps to stratify biomes into low, medium and high change categories. Landsat image pairs can then be sampled within these strata and analyzed for estimating area of forest cleared.

a New Approach for Accuracy Improvement of Pulsed LIDAR Remote Sensing Data

NASA Astrophysics Data System (ADS)

Zhou, G.; Huang, W.; Zhou, X.; He, C.; Li, X.; Huang, Y.; Zhang, L.

2018-05-01

In remote sensing applications, the accuracy of time interval measurement is one of the most important parameters that affect the quality of pulsed lidar data. The traditional time interval measurement technique has the disadvantages of low measurement accuracy, complicated circuit structure and large error. A high-precision time interval data cannot be obtained in these traditional methods. In order to obtain higher quality of remote sensing cloud images based on the time interval measurement, a higher accuracy time interval measurement method is proposed. The method is based on charging the capacitance and sampling the change of capacitor voltage at the same time. Firstly, the approximate model of the capacitance voltage curve in the time of flight of pulse is fitted based on the sampled data. Then, the whole charging time is obtained with the fitting function. In this method, only a high-speed A/D sampler and capacitor are required in a single receiving channel, and the collected data is processed directly in the main control unit. The experimental results show that the proposed method can get error less than 3 ps. Compared with other methods, the proposed method improves the time interval accuracy by at least 20 %.

Satellite Remote Sensing Studies of Biological and Biogeochemical Processing in the Ocean

NASA Technical Reports Server (NTRS)

Vernet, Maria

2001-01-01

The remote sensing of phycoerythrin-containing phytoplankton by ocean color was evaluated. Phycoerythrin (PE) can be remotely sensed by three methods: surface reflectance (Sathyendranath et al. 1994), by laser-activated fluorescence (Hoge and Swift 1986) and by passive fluorescence (Letelier et al. 1996). In collaboration with Dr. Frank Hoge and Robert Swift during Dr. Maria Vernet's tenure as Senior Visiting Scientist at Wallops Island, the active and passive methods were studied, in particular the detection of PE fluorescence and spectral reflectance from airborne LIDAR (AOL). Airborne instrumentation allows for more detailed and flexible sampling of the ocean surface than satellites thus providing the ideal platform to test model and develop algorithms than can later be applied to ocean color by satellites such as TERRA and AQUA. Dr. Vernet's contribution to the Wallops team included determination of PE in the water column, in conjunction with AOL flights in the North Atlantic Bight. In addition, a new flow-through fluorometer for PE determination by fluorescence was tested and calibrated. Results: several goals were achieved during this period. Cruises to the California Current, North Atlantic Bight, Gulf of Maine and Chesapeake Bay provided sampling under different oceanographic and optical conditions. The ships carried the flow-through fluorometer and samples for the determination of PE were obtained from the flow-through flow. The AOL was flown over the ship's track, usually several flights during the cruise, weather permitting.

Combining lake and watershed characteristics with Landsat TM data for remote estimation of regional lake clarity

USGS Publications Warehouse

McCullough, Ian M.; Loftin, Cyndy; Sader, Steven A.

2012-01-01

Water clarity is a reliable indicator of lake productivity and an ideal metric of regional water quality. Clarity is an indicator of other water quality variables including chlorophyll-a, total phosphorus and trophic status; however, unlike these metrics, clarity can be accurately and efficiently estimated remotely on a regional scale. Remote sensing is useful in regions containing a large number of lakes that are cost prohibitive to monitor regularly using traditional field methods. Field-assessed lakes generally are easily accessible and may represent a spatially irregular, non-random sample of a region. We developed a remote monitoring program for Maine lakes >8 ha (1511 lakes) to supplement existing field monitoring programs. We combined Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) brightness values for TM bands 1 (blue) and 3 (red) to estimate water clarity (secchi disk depth) during 1990–2010. Although similar procedures have been applied to Minnesota and Wisconsin lakes, neither state incorporates physical lake variables or watershed characteristics that potentially affect clarity into their models. Average lake depth consistently improved model fitness, and the proportion of wetland area in lake watersheds also explained variability in clarity in some cases. Nine regression models predicted water clarity (R2 = 0.69–0.90) during 1990–2010, with separate models for eastern (TM path 11; four models) and western Maine (TM path 12; five models that captured differences in topography and landscape disturbance. Average absolute difference between model-estimated and observed secchi depth ranged 0.65–1.03 m. Eutrophic and mesotrophic lakes consistently were estimated more accurately than oligotrophic lakes. Our results show that TM bands 1 and 3 can be used to estimate regional lake water clarity outside the Great Lakes Region and that the accuracy of estimates is improved with additional model variables that reflect physical lake characteristics and watershed conditions.

Modeling Trace Element Concentrations in the San Francisco Bay Estuary from Remote Measurement of Suspended Solids

NASA Astrophysics Data System (ADS)

Press, J.; Broughton, J.; Kudela, R. M.

2014-12-01

Suspended and dissolved trace elements are key determinants of water quality in estuarine and coastal waters. High concentrations of trace element pollutants in the San Francisco Bay estuary necessitate consistent and thorough monitoring to mitigate adverse effects on biological systems and the contamination of water and food resources. Although existing monitoring programs collect annual in situ samples from fixed locations, models proposed by Benoit, Kudela, & Flegal (2010) enable calculation of the water column total concentration (WCT) and the water column dissolved concentration (WCD) of 14 trace elements in the San Francisco Bay from a more frequently sampled metric—suspended solids concentration (SSC). This study tests the application of these models with SSC calculated from remote sensing data, with the aim of validating a tool for continuous synoptic monitoring of trace elements in the San Francisco Bay. Using HICO imagery, semi-analytical and empirical SSC algorithms were tested against a USGS dataset. A single-band method with statistically significant linear fit (p < 0.001) was chosen as the proxy for SSC values. The numerical models for WCT and the distribution ratio D were applied in MATLAB with terms to account for regional and seasonal effects, and results were used to calculate WCD. The modeled results were assessed against in situ data from the San Francisco Estuary Regional Monitoring Program. Quantile regression was used to evaluate model sensitivity to the distribution of regions, and outliers displaying regional aberrations were removed before robust regression was applied. Statistically significant and highly correlated results for WCT were found for 10 elements, with goodness of fit greater than or equal to that of the original models of seven elements. WCD was successfully modeled for six elements, with goodness of fit for each exceeding that of the original models. Concentrations of Arsenic, Iron, and Lead in the southern region of the Bay were found to exceed EPA water quality criteria for human health and aquatic life. The results of this study demonstrate the potential of monitoring programs using remote observation of trace element concentrations, and provide the foundation for investigation of pollutant sources and pathways over time.

Performance of One-Class Classifiers for Invasive Species Mapping using Hyperspectral Remote Sensing

NASA Astrophysics Data System (ADS)

Skowronek, S.; Asner, G. P.; Feilhauer, H.

2016-12-01

Reliable distribution maps are crucial for the monitoring and management of invasive plant species. Remote sensing can provide such maps for larger areas. However, most remote sensing approaches focus on species in a prominent phenological stage, and a systematic assessment of the performance of different one-class classifiers for mapping species in a more inconspicuous phenological stage is missing so far. In this study, we used hyperspectral remote sensing data to detect the invasive grass Phalaris aquatica and the invasive herb Centaurea solstitialisin a pre-flowering stage in the Jasper Ridge Biological Preserve in California. We collected presence-only data, 66 plots for C. solstitialis and 30 plots for P. aquatica, to calibrate a distribution model and additional presence-absence data (166 / 173 plots) to validate model performance. All plots have a size of 3 m x 3 m. The hyperspectral remote sensing imagery was acquired using the Carnegie Airborne Observatory (CAO) visible to shortwave infrared (VSWIR) imaging spectrometer (400-2500 nm range) in May 2015 with a ground sampling distance (pixel size) of 1 m x 1 m. To find the best approach for mapping these species, we compared the performance of three different state-of-the-art classifiers working with presence-only data: Maxent, biased support vector machines and boosted regression trees. The resulting overall accuracies were 72 - 74% for C. solstitialis, and 83 - 88% for P. aquatica. For both species the overall performance was slightly better for Maxent and BRT than for biased SVM. The detection rates for low cover plots were considerably higher for C. solstitialis than for P. aquatica. For C. solstitalis, they ranged between 71 and 75% for plots with less than 15% cover, highlighting the potential of remote sensing to contribute to an early detection. The models relied on different areas of the spectrum, but still produced the same general pattern, which implies that more than one property of a species or a mixed plot can be used to create a viable model. We conclude that the different one-class classifiers we tested do allow detecting the target species in a more inconspicuous phenological stage, with similar success rates.

Optimized endogenous post-stratification in forest inventories

Treesearch

Paul L. Patterson

2012-01-01

An example of endogenous post-stratification is the use of remote sensing data with a sample of ground data to build a logistic regression model to predict the probability that a plot is forested and using the predicted probabilities to form categories for post-stratification. An optimized endogenous post-stratified estimator of the proportion of forest has been...

An object-based storage model for distributed remote sensing images

NASA Astrophysics Data System (ADS)

Yu, Zhanwu; Li, Zhongmin; Zheng, Sheng

2006-10-01

It is very difficult to design an integrated storage solution for distributed remote sensing images to offer high performance network storage services and secure data sharing across platforms using current network storage models such as direct attached storage, network attached storage and storage area network. Object-based storage, as new generation network storage technology emerged recently, separates the data path, the control path and the management path, which solves the bottleneck problem of metadata existed in traditional storage models, and has the characteristics of parallel data access, data sharing across platforms, intelligence of storage devices and security of data access. We use the object-based storage in the storage management of remote sensing images to construct an object-based storage model for distributed remote sensing images. In the storage model, remote sensing images are organized as remote sensing objects stored in the object-based storage devices. According to the storage model, we present the architecture of a distributed remote sensing images application system based on object-based storage, and give some test results about the write performance comparison of traditional network storage model and object-based storage model.

Calibration of passive remote observing optical and microwave instrumentation; Proceedings of the Meeting, Orlando, FL, Apr. 3-5, 1991

NASA Technical Reports Server (NTRS)

Guenther, Bruce W. (Editor)

1991-01-01

Various papers on the calibration of passive remote observing optical and microwave instrumentation are presented. Individual topics addressed include: on-board calibration device for a wide field-of-view instrument, calibration for the medium-resolution imaging spectrometer, cryogenic radiometers and intensity-stabilized lasers for EOS radiometric calibrations, radiometric stability of the Shuttle-borne solar backscatter ultraviolet spectrometer, ratioing radiometer for use with a solar diffuser, requirements of a solar diffuser and measurements of some candidate materials, reflectance stability analysis of Spectralon diffuse calibration panels, stray light effects on calibrations using a solar diffuser, radiometric calibration of SPOT 23 HRVs, surface and aerosol models for use in radiative transfer codes. Also addressed are: calibrated intercepts for solar radiometers used in remote sensor calibration, radiometric calibration of an airborne multispectral scanner, in-flight calibration of a helicopter-mounted Daedalus multispectral scanner, technique for improving the calibration of large-area sphere sources, remote colorimetry and its applications, spatial sampling errors for a satellite-borne scanning radiometer, calibration of EOS multispectral imaging sensors and solar irradiance variability.

Ocean Observing Public-Private Collaboration to Improve Tropical Storm and Hurricane Predictions in the Gulf of Mexico

NASA Astrophysics Data System (ADS)

Perry, R.; Leung, P.; McCall, W.; Martin, K. M.; Howden, S. D.; Vandermeulen, R. A.; Kim, H. S. S.; Kirkpatrick, B. A.; Watson, S.; Smith, W.

2016-02-01

In 2008, Shell partnered with NOAA to explore opportunities for improving storm predictions in the Gulf of Mexico. Since, the collaboration has grown to include partners from Shell, NOAA National Data Buoy Center and National Center for Environmental Information, National Center for Environmental Prediction, University of Southern Mississippi, and the Gulf of Mexico Coastal Ocean Observing System. The partnership leverages complementary strengths of each collaborator to build a comprehensive and sustainable monitoring and data program to expand observing capacity and protect offshore assets and Gulf communities from storms and hurricanes. The program combines in situ and autonomous platforms with remote sensing and numerical modeling. Here we focus on profiling gliders and the benefits of a public-private partnership model for expanding regional ocean observing capacity. Shallow and deep gliders measure ocean temperature to derive ocean heat content (OHC), along with salinity, dissolved oxygen, fluorescence, and CDOM, in the central and eastern Gulf shelf and offshore. Since 2012, gliders have collected 4500+ vertical profiles and surveyed 5000+ nautical miles. Adaptive sampling and mission coordination with NCEP modelers provides specific datasets to assimilate into EMC's coupled HYCOM-HWRF model and 'connect-the-dots' between well-established Eulerian metocean measurements by obtaining (and validating) data between fixed stations (e.g. platform and buoy ADCPs) . Adaptive sampling combined with remote sensing provides satellite-derived OHC validation and the ability to sample productive coastal waters advected offshore by the Loop Current. Tracking coastal waters with remote sensing provides another verification of estimate Loop Current and eddy boundaries, as well as quantifying productivity and analyzing water quality on the Gulf coast, shelf break and offshore. Incorporating gliders demonstrates their value as tools to better protect offshore oil and gas assets and the greater Gulf coast communities from storms and hurricanes. Data collected under the collaboration, along with deployment of gliders, will have long-term benefits in helping to understand the ecological and environmental health of the Gulf by monitoring real-time annual and seasonal physical variability.

Strategies for using remotely sensed data in hydrologic models

NASA Technical Reports Server (NTRS)

Peck, E. L.; Keefer, T. N.; Johnson, E. R. (Principal Investigator)

1981-01-01

Present and planned remote sensing capabilities were evaluated. The usefulness of six remote sensing capabilities (soil moisture, land cover, impervious area, areal extent of snow cover, areal extent of frozen ground, and water equivalent of the snow cover) with seven hydrologic models (API, CREAMS, NWSRFS, STORM, STANFORD, SSARR, and NWSRFS Snowmelt) were reviewed. The results indicate remote sensing information has only limited value for use with the hydrologic models in their present form. With minor modifications to the models the usefulness would be enhanced. Specific recommendations are made for incorporating snow covered area measurements in the NWSRFS Snowmelt model. Recommendations are also made for incorporating soil moisture measurements in NWSRFS. Suggestions are made for incorporating snow covered area, soil moisture, and others in STORM and SSARR. General characteristics of a hydrologic model needed to make maximum use of remotely sensed data are discussed. Suggested goals for improvements in remote sensing for use in models are also established.

How Much Can Remotely-Sensed Natural Resource Inventories Benefit from Finer Spatial Resolutions?

NASA Astrophysics Data System (ADS)

Hou, Z.; Xu, Q.; McRoberts, R. E.; Ståhl, G.; Greenberg, J. A.

2017-12-01

For remote sensing facilitated natural resource inventories, the effects of spatial resolution in the form of pixel size and the effects of subpixel information on estimates of population parameters were evaluated by comparing results obtained using Landsat 8 and RapidEye auxiliary imagery. The study area was in Burkina Faso, and the variable of interest was the stem volume (m3/ha) convertible to the woodland aboveground biomass. A sample consisting of 160 field plots was selected and measured from the population following a two-stage sampling design. Models were fit using weighted least squares; the population mean, mu, and the variance of the estimator of the population mean, Var(mu.hat), were estimated in two inferential frameworks, model-based and model-assisted, and compared; for each framework, Var(mu.hat) was estimated both analytically and empirically. Empirical variances were estimated with bootstrapping that for resampling takes clustering effects into account. The primary results were twofold. First, for the effects of spatial resolution and subpixel information, four conclusions are relevant: (1) finer spatial resolution imagery indeed contributes to greater precision for estimators of population parameter, but this increase is slight at a maximum rate of 20% considering that RapidEye data are 36 times finer resolution than Landsat 8 data; (2) subpixel information on texture is marginally beneficial when it comes to making inference for population of large areas; (3) cost-effectiveness is more favorable for the free of charge Landsat 8 imagery than RapidEye imagery; and (4) for a given plot size, candidate remote sensing auxiliary datasets are more cost-effective when their spatial resolutions are similar to the plot size than with much finer alternatives. Second, for the comparison between estimators, three conclusions are relevant: (1) model-based variance estimates are consistent with each other and about half as large as stabilized model-assisted estimates, suggesting superior effectiveness of model-based inference to model-assisted inference; (2) bootstrapping is an effective alternative to analytical variance estimators; and (3) prediction accuracy expressed by RMSE is useful for screening candidate models to be used for population inferences.

Multi-scale assimilation of remotely sensed snow observations for hydrologic estimation

NASA Astrophysics Data System (ADS)

Andreadis, K.; Lettenmaier, D.

2008-12-01

Data assimilation provides a framework for optimally merging model predictions and remote sensing observations of snow properties (snow cover extent, water equivalent, grain size, melt state), ideally overcoming limitations of both. A synthetic twin experiment is used to evaluate a data assimilation system that would ingest remotely sensed observations from passive microwave and visible wavelength sensors (brightness temperature and snow cover extent derived products, respectively) with the objective of estimating snow water equivalent. Two data assimilation techniques are used, the Ensemble Kalman filter and the Ensemble Multiscale Kalman filter (EnMKF). One of the challenges inherent in such a data assimilation system is the discrepancy in spatial scales between the different types of snow-related observations. The EnMKF represents the sample model error covariance with a tree that relates the system state variables at different locations and scales through a set of parent-child relationships. This provides an attractive framework to efficiently assimilate observations at different spatial scales. This study provides a first assessment of the feasibility of a system that would assimilate observations from multiple sensors (MODIS snow cover and AMSR-E brightness temperatures) and at different spatial scales for snow water equivalent estimation. The relative value of the different types of observations is examined. Additionally, the error characteristics of both model and observations are discussed.

'Achievement, pride and inspiration': outcomes for volunteer role models in a community outreach program in remote Aboriginal communities.

PubMed

Cinelli, Renata L; Peralta, Louisa R

2015-01-01

There is growing support for the prosocial value of role modelling in programs for adolescents and the potentially positive impact role models can have on health and health behaviours in remote communities. Despite known benefits for remote outreach program recipients, there is limited literature on the outcomes of participation for role models. Twenty-four role models participated in a remote outreach program across four remote Aboriginal communities in the Northern Territory, Australia (100% recruitment). Role models participated in semi-structured one-on-one interviews. Transcripts were coded and underwent thematic analysis by both authors. Cultural training, Indigenous heritage and prior experience contributed to general feelings of preparedness, yet some role models experienced a level of culture shock, being confronted by how disparate the communities were to their home communities. Benefits of participation included exposure to and experience with remote Aboriginal peoples and community, increased cultural knowledge, personal learning, forming and building relationships, and skill development. Effective role model programs designed for remote Indigenous youth can have positive outcomes for both role models and the program recipients. Cultural safety training is an important factor for preparing role models and for building their cultural competency for implementing health and education programs in remote Indigenous communities in Australia. This will maximise the opportunities for participants to achieve outcomes and minimise their culture shock.

Effective Sequential Classifier Training for SVM-Based Multitemporal Remote Sensing Image Classification

NASA Astrophysics Data System (ADS)

Guo, Yiqing; Jia, Xiuping; Paull, David

2018-06-01

The explosive availability of remote sensing images has challenged supervised classification algorithms such as Support Vector Machines (SVM), as training samples tend to be highly limited due to the expensive and laborious task of ground truthing. The temporal correlation and spectral similarity between multitemporal images have opened up an opportunity to alleviate this problem. In this study, a SVM-based Sequential Classifier Training (SCT-SVM) approach is proposed for multitemporal remote sensing image classification. The approach leverages the classifiers of previous images to reduce the required number of training samples for the classifier training of an incoming image. For each incoming image, a rough classifier is firstly predicted based on the temporal trend of a set of previous classifiers. The predicted classifier is then fine-tuned into a more accurate position with current training samples. This approach can be applied progressively to sequential image data, with only a small number of training samples being required from each image. Experiments were conducted with Sentinel-2A multitemporal data over an agricultural area in Australia. Results showed that the proposed SCT-SVM achieved better classification accuracies compared with two state-of-the-art model transfer algorithms. When training data are insufficient, the overall classification accuracy of the incoming image was improved from 76.18% to 94.02% with the proposed SCT-SVM, compared with those obtained without the assistance from previous images. These results demonstrate that the leverage of a priori information from previous images can provide advantageous assistance for later images in multitemporal image classification.

A BATTERY-OPERATED AIR SAMPLER FOR REMOTE AREAS

EPA Science Inventory

An air sampling system developed to evaluate air quality in biosphere reserves or in other remote areas is described. The equipment consists of a Dupont P-4000 pump and a specially designed battery pack containing Gates batteries. This air sampling system was tested in Southern U...

Modularity of Protein Folds as a Tool for Template-Free Modeling of Structures.

PubMed

Vallat, Brinda; Madrid-Aliste, Carlos; Fiser, Andras

2015-08-01

Predicting the three-dimensional structure of proteins from their amino acid sequences remains a challenging problem in molecular biology. While the current structural coverage of proteins is almost exclusively provided by template-based techniques, the modeling of the rest of the protein sequences increasingly require template-free methods. However, template-free modeling methods are much less reliable and are usually applicable for smaller proteins, leaving much space for improvement. We present here a novel computational method that uses a library of supersecondary structure fragments, known as Smotifs, to model protein structures. The library of Smotifs has saturated over time, providing a theoretical foundation for efficient modeling. The method relies on weak sequence signals from remotely related protein structures to create a library of Smotif fragments specific to the target protein sequence. This Smotif library is exploited in a fragment assembly protocol to sample decoys, which are assessed by a composite scoring function. Since the Smotif fragments are larger in size compared to the ones used in other fragment-based methods, the proposed modeling algorithm, SmotifTF, can employ an exhaustive sampling during decoy assembly. SmotifTF successfully predicts the overall fold of the target proteins in about 50% of the test cases and performs competitively when compared to other state of the art prediction methods, especially when sequence signal to remote homologs is diminishing. Smotif-based modeling is complementary to current prediction methods and provides a promising direction in addressing the structure prediction problem, especially when targeting larger proteins for modeling.

Multivariate Spatio-Temporal Clustering: A Framework for Integrating Disparate Data to Understand Network Representativeness and Scaling Up Sparse Ecosystem Measurements

NASA Astrophysics Data System (ADS)

Hoffman, F. M.; Kumar, J.; Maddalena, D. M.; Langford, Z.; Hargrove, W. W.

2014-12-01

Disparate in situ and remote sensing time series data are being collected to understand the structure and function of ecosystems and how they may be affected by climate change. However, resource and logistical constraints limit the frequency and extent of observations, particularly in the harsh environments of the arctic and the tropics, necessitating the development of a systematic sampling strategy to maximize coverage and objectively represent variability at desired scales. These regions host large areas of potentially vulnerable ecosystems that are poorly represented in Earth system models (ESMs), motivating two new field campaigns, called Next Generation Ecosystem Experiments (NGEE) for the Arctic and Tropics, funded by the U.S. Department of Energy. Multivariate Spatio-Temporal Clustering (MSTC) provides a quantitative methodology for stratifying sampling domains, informing site selection, and determining the representativeness of measurement sites and networks. We applied MSTC to down-scaled general circulation model results and data for the State of Alaska at a 4 km2 resolution to define maps of ecoregions for the present (2000-2009) and future (2090-2099), showing how combinations of 37 bioclimatic characteristics are distributed and how they may shift in the future. Optimal representative sampling locations were identified on present and future ecoregion maps, and representativeness maps for candidate sampling locations were produced. We also applied MSTC to remotely sensed LiDAR measurements and multi-spectral imagery from the WorldView-2 satellite at a resolution of about 5 m2 within the Barrow Environmental Observatory (BEO) in Alaska. At this resolution, polygonal ground features—such as centers, edges, rims, and troughs—can be distinguished. Using these remote sensing data, we up-scaled vegetation distribution data collected on these polygonal ground features to a large area of the BEO to provide distributions of plant functional types that can be used to parameterize ESMs. In addition, we applied MSTC to 4 km2 global bioclimate data to define global ecoregions and understand the representativeness of CTFS-ForestGEO, Fluxnet, and RAINFOR sampling networks. These maps identify tropical forests underrepresented in existing observations of individual and combined networks.

Cryogenic Liquid Sample Acquisition System for Remote Space Applications

NASA Technical Reports Server (NTRS)

Mahaffy, Paul; Trainer, Melissa; Wegel, Don; Hawk, Douglas; Melek, Tony; Johnson, Christopher; Amato, Michael; Galloway, John

2013-01-01

There is a need to acquire autonomously cryogenic hydrocarbon liquid sample from remote planetary locations such as the lakes of Titan for instruments such as mass spectrometers. There are several problems that had to be solved relative to collecting the right amount of cryogenic liquid sample into a warmer spacecraft, such as not allowing the sample to boil off or fractionate too early; controlling the intermediate and final pressures within carefully designed volumes; designing for various particulates and viscosities; designing to thermal, mass, and power-limited spacecraft interfaces; and reducing risk. Prior art inlets for similar instruments in spaceflight were designed primarily for atmospheric gas sampling and are not useful for this front-end application. These cryogenic liquid sample acquisition system designs for remote space applications allow for remote, autonomous, controlled sample collections of a range of challenging cryogenic sample types. The design can control the size of the sample, prevent fractionation, control pressures at various stages, and allow for various liquid sample levels. It is capable of collecting repeated samples autonomously in difficult lowtemperature conditions often found in planetary missions. It is capable of collecting samples for use by instruments from difficult sample types such as cryogenic hydrocarbon (methane, ethane, and propane) mixtures with solid particulates such as found on Titan. The design with a warm actuated valve is compatible with various spacecraft thermal and structural interfaces. The design uses controlled volumes, heaters, inlet and vent tubes, a cryogenic valve seat, inlet screens, temperature and cryogenic liquid sensors, seals, and vents to accomplish its task.

Fiber-Coupled Acousto-Optical-Filter Spectrometer

NASA Technical Reports Server (NTRS)

Levin, Kenneth H.; Li, Frank Yanan

1993-01-01

Fiber-coupled acousto-optical-filter spectrometer steps rapidly through commanded sequence of wavelengths. Sample cell located remotely from monochromator and associated electronic circuitry, connected to them with optical fibers. Optical-fiber coupling makes possible to monitor samples in remote, hazardous, or confined locations. Advantages include compactness, speed, and no moving parts. Potential applications include control of chemical processes, medical diagnoses, spectral imaging, and sampling of atmospheres.

Analysis of variograms with various sample sizes from a multispectral image

USDA-ARS?s Scientific Manuscript database

Variogram plays a crucial role in remote sensing application and geostatistics. It is very important to estimate variogram reliably from sufficient data. In this study, the analysis of variograms with various sample sizes of remotely sensed data was conducted. A 100x100-pixel subset was chosen from ...

Analysis of variograms with various sample sizes from a multispectral image

USDA-ARS?s Scientific Manuscript database

Variograms play a crucial role in remote sensing application and geostatistics. In this study, the analysis of variograms with various sample sizes of remotely sensed data was conducted. A 100 X 100 pixel subset was chosen from an aerial multispectral image which contained three wavebands, green, ...

30 CFR 57.22227 - Approved testing devices (I-A, I-B, I-C, II-A, II-B, III, IV, V-A, and V-B mines).

Code of Federal Regulations, 2010 CFR

2010-07-01

... be used in Subcategory I-C mines. (c)(1) If electrically powered, remote sensing devices are used.... (2) If air samples are delivered to remote analytical devices through sampling tubes, such tubes...

POLYCHLORINATED DIBENZO-P-DIOXINS AND DIBENZOFURANS IN THE REMOTE NORTH ATLANTIC MARINE ATMOSPHERE (R825377)

EPA Science Inventory

We have developed a sampling strategy that allows us to determine
femtogram/cubic meter concentrations of polychlorinated dibenzo-p-dioxins
and polychlorinated dibenzofurans (PCDD/F) in remote marine atmospheres. Using
this sampling strategy, a total of 37 a...

Development of a drought forecasting model for the Asia-Pacific region using remote sensing and climate data: Focusing on Indonesia

NASA Astrophysics Data System (ADS)

Rhee, Jinyoung; Kim, Gayoung; Im, Jungho

2017-04-01

Three regions of Indonesia with different rainfall characteristics were chosen to develop drought forecast models based on machine learning. The 6-month Standardized Precipitation Index (SPI6) was selected as the target variable. The models' forecast skill was compared to the skill of long-range climate forecast models in terms of drought accuracy and regression mean absolute error (MAE). Indonesian droughts are known to be related to El Nino Southern Oscillation (ENSO) variability despite of regional differences as well as monsoon, local sea surface temperature (SST), other large-scale atmosphere-ocean interactions such as Indian Ocean Dipole (IOD) and Southern Pacific Convergence Zone (SPCZ), and local factors including topography and elevation. Machine learning models are thus to enhance drought forecast skill by combining local and remote SST and remote sensing information reflecting initial drought conditions to the long-range climate forecast model results. A total of 126 machine learning models were developed for the three regions of West Java (JB), West Sumatra (SB), and Gorontalo (GO) and six long-range climate forecast models of MSC_CanCM3, MSC_CanCM4, NCEP, NASA, PNU, POAMA as well as one climatology model based on remote sensing precipitation data, and 1 to 6-month lead times. When compared the results between the machine learning models and the long-range climate forecast models, West Java and Gorontalo regions showed similar characteristics in terms of drought accuracy. Drought accuracy of the long-range climate forecast models were generally higher than the machine learning models with short lead times but the opposite appeared for longer lead times. For West Sumatra, however, the machine learning models and the long-range climate forecast models showed similar drought accuracy. The machine learning models showed smaller regression errors for all three regions especially with longer lead times. Among the three regions, the machine learning models developed for Gorontalo showed the highest drought accuracy and the lowest regression error. West Java showed higher drought accuracy compared to West Sumatra, while West Sumatra showed lower regression error compared to West Java. The lower error in West Sumatra may be because of the smaller sample size used for training and evaluation for the region. Regional differences of forecast skill are determined by the effect of ENSO and the following forecast skill of the long-range climate forecast models. While shown somewhat high in West Sumatra, relative importance of remote sensing variables was mostly low in most cases. High importance of the variables based on long-range climate forecast models indicates that the forecast skill of the machine learning models are mostly determined by the forecast skill of the climate models.

Bringing the magic of light to remote areas where resources are scarce: beautiful demonstrations of interference patterns using laser pens and fibres

NASA Astrophysics Data System (ADS)

Mignard, D.

2016-09-01

The training of physics teachers in remote areas in the developing world requires dedicated trainers (who typically are volunteers), as well as robust logistics. The latter must include the supply of equipment for experiments in the classroom. This task is greatly aided by the use of cheap, safe and readily available consumer goods that do not require local power supplies. In this paper, a simple experiment using a laser pointer pen and samples of hair as well as wire and transparent thin fibre is presented, reproducing a variant of Thomas Youngs’ famed double slit experiment. The spread of the interference pattern as it projects itself on a screen is sufficiently large to catch the interest of students, and its orientation being perpendicular to that of the hair is also strikingly counter-intuitive. The students are then encouraged to apply the simplified Fraunhofer equation to the various samples to find out the width of their hair. Ideally, these samples would also include calibrating materials like fibres and wires of known diameters, the use of which should give confidence in the model by confirming that it can predict the sample diameter. A fruitful discussion supported by diagrams can also be conducted on the differences that could be expected between a straight edge and a rounded edge, the latter throwing an unexpected challenge to the initial model. However, the use of a transparent fibre also clearly illustrate the limitations of this model, a perception that is amplified by the particularly wide and bright interference pattern that it produces. This mismatch between the model and the real system should prompt the students to further refine their description of the physical system and the resulting model. Throughout the session, their reasoning may be helped by encouraging them to produce diagrams showing the path of optical rays.

Light Scattering by Gaussian Particles: A Solution with Finite-Difference Time Domain Technique

NASA Technical Reports Server (NTRS)

Sun, W.; Nousiainen, T.; Fu, Q.; Loeb, N. G.; Videen, G.; Muinonen, K.

2003-01-01

The understanding of single-scattering properties of complex ice crystals has significance in atmospheric radiative transfer and remote-sensing applications. In this work, light scattering by irregularly shaped Gaussian ice crystals is studied with the finite-difference time-domain (FDTD) technique. For given sample particle shapes and size parameters in the resonance region, the scattering phase matrices and asymmetry factors are calculated. It is found that the deformation of the particle surface can significantly smooth the scattering phase functions and slightly reduce the asymmetry factors. The polarization properties of irregular ice crystals are also significantly different from those of spherical cloud particles. These FDTD results could provide a reference for approximate light-scattering models developed for irregular particle shapes and can have potential applications in developing a much simpler practical light scattering model for ice clouds angular-distribution models and for remote sensing of ice clouds and aerosols using polarized light. (copyright) 2003 Elsevier Science Ltd. All rights reserved.

Do deep convolutional neural networks really need to be deep when applied for remote scene classification?

NASA Astrophysics Data System (ADS)

Luo, Chang; Wang, Jie; Feng, Gang; Xu, Suhui; Wang, Shiqiang

2017-10-01

Deep convolutional neural networks (CNNs) have been widely used to obtain high-level representation in various computer vision tasks. However, for remote scene classification, there are not sufficient images to train a very deep CNN from scratch. From two viewpoints of generalization power, we propose two promising kinds of deep CNNs for remote scenes and try to find whether deep CNNs need to be deep for remote scene classification. First, we transfer successful pretrained deep CNNs to remote scenes based on the theory that depth of CNNs brings the generalization power by learning available hypothesis for finite data samples. Second, according to the opposite viewpoint that generalization power of deep CNNs comes from massive memorization and shallow CNNs with enough neural nodes have perfect finite sample expressivity, we design a lightweight deep CNN (LDCNN) for remote scene classification. With five well-known pretrained deep CNNs, experimental results on two independent remote-sensing datasets demonstrate that transferred deep CNNs can achieve state-of-the-art results in an unsupervised setting. However, because of its shallow architecture, LDCNN cannot obtain satisfactory performance, regardless of whether in an unsupervised, semisupervised, or supervised setting. CNNs really need depth to obtain general features for remote scenes. This paper also provides baseline for applying deep CNNs to other remote sensing tasks.

Effect of L-cysteine on remote organ injury in rats with severe acute pancreatitis induced by bile-pancreatic duct obstruction.

PubMed

Yang, Li-Juan; Wan, Rong; Shen, Jia-Qing; Shen, Jie; Wang, Xing-Peng

2013-08-01

Remote organ failure occurs in cases of acute pancreatitis (AP); however, the reports on AP induced by pancreatic duct obstruction are rare. In this study we determined the effect of L-cysteine on pancreaticobiliary inflammation and remote organ damage in rats after pancreaticobiliary duct ligation (PBDL). AP was induced by PBDL in rats with 5/0 silk. Sixty rats were randomly divided into 4 groups. Groups A and B were sham-operated groups that received injections of saline or L-cysteine (10 mg/kg) intraperitoneally (15 rats in each group). Groups C and D were PBDL groups that received injections of saline or L-cysteine (10 mg/kg) intraperitoneally (15 rats in each group). The tissue samples of the pancreas and remote organs such as the lung, liver, intestine and kidney were subsequently examined for pathological changes under a light microscope. The samples were also stored for the determination of malondialdehyde and glutathione levels. Blood urea nitrogen (BUN), plasma amylase, ALT and AST levels were determined spectrophotometrically using an automated analyzer. Also, we evaluated the effect of L-cysteine on remote organ injury in rats with AP induced by retrograde infusion of 3.5% sodium taurocholate (NaTc) into the bile-pancreatic duct. Varying degrees of injury in the pancreas, lung, liver, intestine and kidney were observed in the rats 24 hours after PBDL. The severity of injury to the lung, liver and intestine was attenuated, while injury status was not changed significantly in the pancreas and kidney after L-cysteine treatment. Oxidative stress was also affected by L-cysteine in PBDL-treated rats. The concentration of tissue malondialdehyde decreased in the pancreas and remote organs of PBDL and L-cysteine administrated rats, and the concentration of glutathione increased more significantly than that of the model control group. However, L-cysteine administration reduced the severity of injury in remote organs but not in the pancreas in rats with NaTc-induced AP. L-cysteine treatment attenuated multiple organ damage at an early stage of AP in rats and modulated the oxidant/antioxidant imbalance.

Remote sensing of the Canadian Arctic: Modelling biophysical variables

NASA Astrophysics Data System (ADS)

Liu, Nanfeng

It is anticipated that Arctic vegetation will respond in a variety of ways to altered temperature and precipitation patterns expected with climate change, including changes in phenology, productivity, biomass, cover and net ecosystem exchange. Remote sensing provides data and data processing methodologies for monitoring and assessing Arctic vegetation over large areas. The goal of this research was to explore the potential of hyperspectral and high spatial resolution multispectral remote sensing data for modelling two important Arctic biophysical variables: Percent Vegetation Cover (PVC) and the fraction of Absorbed Photosynthetically Active Radiation (fAPAR). A series of field experiments were conducted to collect PVC and fAPAR at three Canadian Arctic sites: (1) Sabine Peninsula, Melville Island, NU; (2) Cape Bounty Arctic Watershed Observatory (CBAWO), Melville Island, NU; and (3) Apex River Watershed (ARW), Baffin Island, NU. Linear relationships between biophysical variables and Vegetation Indices (VIs) were examined at different spatial scales using field spectra (for the Sabine Peninsula site) and high spatial resolution satellite data (for the CBAWO and ARW sites). At the Sabine Peninsula site, hyperspectral VIs exhibited a better performance for modelling PVC than multispectral VIs due to their capacity for sampling fine spectral features. The optimal hyperspectral bands were located at important spectral features observed in Arctic vegetation spectra, including leaf pigment absorption in the red wavelengths and at the red-edge, leaf water absorption in the near infrared, and leaf cellulose and lignin absorption in the shortwave infrared. At the CBAWO and ARW sites, field PVC and fAPAR exhibited strong correlations (R2 > 0.70) with the NDVI (Normalized Difference Vegetation Index) derived from high-resolution WorldView-2 data. Similarly, high spatial resolution satellite-derived fAPAR was correlated to MODIS fAPAR (R2 = 0.68), with a systematic overestimation of 0.08, which was attributed to PAR absorption by soil that could not be excluded from the fAPAR calculation. This research clearly demonstrates that high spectral and spatial resolution remote sensing VIs can be used to successfully model Arctic biophysical variables. The methods and results presented in this research provided a guide for future studies aiming to model other Arctic biophysical variables through remote sensing data.

Estimation of water turbidity in Gorgan Bay, South-east of Caspian Sea by using IRS-LISS-III images.

PubMed

Aghighi, Hossein; Alimohammadi, Abbas; Saradjian, Mohammad Reza; Ashourloo, Davood

2008-03-01

In this research, usefulness of IRS-LISS-III data of Gorgan Bay, South-east of Caspian Sea located in North of Iran for water turbidity mapping, has been tested. After correction of geometric and radiometric errors, the resulting radiance data were used for examination of correlations between the remotely sensed and in situ water turbidity data simultaneously measured by the Secchi depth approach. Results of this research showed good relations between the Secchi depth and spectral data. The fitted statistical model was very significant (R2 = 0.77) and test of the model performance by independent samples was encouraging. Because of the low costs encountered with acquisition and processing of remotely sensed data, further research in larger scales for the purpose of more precise test of the approach for water turbidity mapping and monitoring is recommended.

Research Advances on Radiation Transfer Modeling and Inversion for Multi-Scale Land Surface Remote Sensing

NASA Astrophysics Data System (ADS)

Liu, Q.

2011-09-01

At first, research advances on radiation transfer modeling on multi-scale remote sensing data are presented: after a general overview of remote sensing radiation transfer modeling, several recent research advances are presented, including leaf spectrum model (dPROS-PECT), vegetation canopy BRDF models, directional thermal infrared emission models(TRGM, SLEC), rugged mountains area radiation models, and kernel driven models etc. Then, new methodologies on land surface parameters inversion based on multi-source remote sensing data are proposed. The land surface Albedo, leaf area index, temperature/emissivity, and surface net radiation etc. are taken as examples. A new synthetic land surface parameter quantitative remote sensing product generation system is designed and the software system prototype will be demonstrated. At last, multi-scale field experiment campaigns, such as the field campaigns in Gansu and Beijing, China will be introduced briefly. The ground based, tower based, and airborne multi-angular measurement system have been built to measure the directional reflectance, emission and scattering characteristics from visible, near infrared, thermal infrared and microwave bands for model validation and calibration. The remote sensing pixel scale "true value" measurement strategy have been designed to gain the ground "true value" of LST, ALBEDO, LAI, soil moisture and ET etc. at 1-km2 for remote sensing product validation.

Multivariate analysis of remote LIBS spectra using partial least squares, principal component analysis, and related techniques

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

Clegg, Samuel M; Barefield, James E; Wiens, Roger C

2008-01-01

Quantitative analysis with LIBS traditionally employs calibration curves that are complicated by the chemical matrix effects. These chemical matrix effects influence the LIBS plasma and the ratio of elemental composition to elemental emission line intensity. Consequently, LIBS calibration typically requires a priori knowledge of the unknown, in order for a series of calibration standards similar to the unknown to be employed. In this paper, three new Multivariate Analysis (MV A) techniques are employed to analyze the LIBS spectra of 18 disparate igneous and highly-metamorphosed rock samples. Partial Least Squares (PLS) analysis is used to generate a calibration model from whichmore » unknown samples can be analyzed. Principal Components Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA) are employed to generate a model and predict the rock type of the samples. These MV A techniques appear to exploit the matrix effects associated with the chemistries of these 18 samples.« less

A Hybrid Semi-supervised Classification Scheme for Mining Multisource Geospatial Data

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

Vatsavai, Raju; Bhaduri, Budhendra L

2011-01-01

Supervised learning methods such as Maximum Likelihood (ML) are often used in land cover (thematic) classification of remote sensing imagery. ML classifier relies exclusively on spectral characteristics of thematic classes whose statistical distributions (class conditional probability densities) are often overlapping. The spectral response distributions of thematic classes are dependent on many factors including elevation, soil types, and ecological zones. A second problem with statistical classifiers is the requirement of large number of accurate training samples (10 to 30 |dimensions|), which are often costly and time consuming to acquire over large geographic regions. With the increasing availability of geospatial databases, itmore » is possible to exploit the knowledge derived from these ancillary datasets to improve classification accuracies even when the class distributions are highly overlapping. Likewise newer semi-supervised techniques can be adopted to improve the parameter estimates of statistical model by utilizing a large number of easily available unlabeled training samples. Unfortunately there is no convenient multivariate statistical model that can be employed for mulitsource geospatial databases. In this paper we present a hybrid semi-supervised learning algorithm that effectively exploits freely available unlabeled training samples from multispectral remote sensing images and also incorporates ancillary geospatial databases. We have conducted several experiments on real datasets, and our new hybrid approach shows over 25 to 35% improvement in overall classification accuracy over conventional classification schemes.« less

Remote sensing of Earth terrain

NASA Technical Reports Server (NTRS)

Kong, J. A.

1992-01-01

Research findings are summarized for projects dealing with the following: application of theoretical models to active and passive remote sensing of saline ice; radiative transfer theory for polarimetric remote sensing of pine forest; scattering of electromagnetic waves from a dense medium consisting of correlated Mie scatterers with size distribution and applications to dry snow; variance of phase fluctuations of waves propagating through a random medium; theoretical modeling for passive microwave remote sensing of earth terrain; polarimetric signatures of a canopy of dielectric cylinders based on first and second order vector radiative transfer theory; branching model for vegetation; polarimetric passive remote sensing of periodic surfaces; composite volume and surface scattering model; and radar image classification.

Estimating spatially distributed soil texture using time series of thermal remote sensing - a case study in central Europe

NASA Astrophysics Data System (ADS)

Müller, Benjamin; Bernhardt, Matthias; Jackisch, Conrad; Schulz, Karsten

2016-09-01

For understanding water and solute transport processes, knowledge about the respective hydraulic properties is necessary. Commonly, hydraulic parameters are estimated via pedo-transfer functions using soil texture data to avoid cost-intensive measurements of hydraulic parameters in the laboratory. Therefore, current soil texture information is only available at a coarse spatial resolution of 250 to 1000 m. Here, a method is presented to derive high-resolution (15 m) spatial topsoil texture patterns for the meso-scale Attert catchment (Luxembourg, 288 km2) from 28 images of ASTER (advanced spaceborne thermal emission and reflection radiometer) thermal remote sensing. A principle component analysis of the images reveals the most dominant thermal patterns (principle components, PCs) that are related to 212 fractional soil texture samples. Within a multiple linear regression framework, distributed soil texture information is estimated and related uncertainties are assessed. An overall root mean squared error (RMSE) of 12.7 percentage points (pp) lies well within and even below the range of recent studies on soil texture estimation, while requiring sparser sample setups and a less diverse set of basic spatial input. This approach will improve the generation of spatially distributed topsoil maps, particularly for hydrologic modeling purposes, and will expand the usage of thermal remote sensing products.

Scaling up functional traits for ecosystem services with remote sensing: concepts and methods.

PubMed

Abelleira Martínez, Oscar J; Fremier, Alexander K; Günter, Sven; Ramos Bendaña, Zayra; Vierling, Lee; Galbraith, Sara M; Bosque-Pérez, Nilsa A; Ordoñez, Jenny C

2016-07-01

Ecosystem service-based management requires an accurate understanding of how human modification influences ecosystem processes and these relationships are most accurate when based on functional traits. Although trait variation is typically sampled at local scales, remote sensing methods can facilitate scaling up trait variation to regional scales needed for ecosystem service management. We review concepts and methods for scaling up plant and animal functional traits from local to regional spatial scales with the goal of assessing impacts of human modification on ecosystem processes and services. We focus our objectives on considerations and approaches for (1) conducting local plot-level sampling of trait variation and (2) scaling up trait variation to regional spatial scales using remotely sensed data. We show that sampling methods for scaling up traits need to account for the modification of trait variation due to land cover change and species introductions. Sampling intraspecific variation, stratification by land cover type or landscape context, or inference of traits from published sources may be necessary depending on the traits of interest. Passive and active remote sensing are useful for mapping plant phenological, chemical, and structural traits. Combining these methods can significantly improve their capacity for mapping plant trait variation. These methods can also be used to map landscape and vegetation structure in order to infer animal trait variation. Due to high context dependency, relationships between trait variation and remotely sensed data are not directly transferable across regions. We end our review with a brief synthesis of issues to consider and outlook for the development of these approaches. Research that relates typical functional trait metrics, such as the community-weighted mean, with remote sensing data and that relates variation in traits that cannot be remotely sensed to other proxies is needed. Our review narrows the gap between functional trait and remote sensing methods for ecosystem service management.

Modeling Coniferous Canopy Structure over Extensive Areas for Ray Tracing Simulations: Scaling from the Leaf to the Stand Level

NASA Astrophysics Data System (ADS)

van Aardt, J. A.; van Leeuwen, M.; Kelbe, D.; Kampe, T.; Krause, K.

2015-12-01

Remote sensing is widely accepted as a useful technology for characterizing the Earth surface in an objective, reproducible, and economically feasible manner. To date, the calibration and validation of remote sensing data sets and biophysical parameter estimates remain challenging due to the requirements to sample large areas for ground-truth data collection, and restrictions to sample these data within narrow temporal windows centered around flight campaigns or satellite overpasses. The computer graphics community have taken significant steps to ameliorate some of these challenges by providing an ability to generate synthetic images based on geometrically and optically realistic representations of complex targets and imaging instruments. These synthetic data can be used for conceptual and diagnostic tests of instrumentation prior to sensor deployment or to examine linkages between biophysical characteristics of the Earth surface and at-sensor radiance. In the last two decades, the use of image generation techniques for remote sensing of the vegetated environment has evolved from the simulation of simple homogeneous, hypothetical vegetation canopies, to advanced scenes and renderings with a high degree of photo-realism. Reported virtual scenes comprise up to 100M surface facets; however, due to the tighter coupling between hardware and software development, the full potential of image generation techniques for forestry applications yet remains to be fully explored. In this presentation, we examine the potential computer graphics techniques have for the analysis of forest structure-function relationships and demonstrate techniques that provide for the modeling of extremely high-faceted virtual forest canopies, comprising billions of scene elements. We demonstrate the use of ray tracing simulations for the analysis of gap size distributions and characterization of foliage clumping within spatial footprints that allow for a tight matching between characteristics derived from these virtual scenes and typical pixel resolutions of remote sensing imagery.

Research Advances on Radiation Transfer Modeling and Inversion for Multi-scale Land Surface Remote Sensing

NASA Astrophysics Data System (ADS)

Liu, Q.; Li, J.; Du, Y.; Wen, J.; Zhong, B.; Wang, K.

2011-12-01

As the remote sensing data accumulating, it is a challenge and significant issue how to generate high accurate and consistent land surface parameter product from the multi source remote observation and the radiation transfer modeling and inversion methodology are the theoretical bases. In this paper, recent research advances and unresolved issues are presented. At first, after a general overview, recent research advances on multi-scale remote sensing radiation transfer modeling are presented, including leaf spectrum model, vegetation canopy BRDF models, directional thermal infrared emission models, rugged mountains area radiation models, and kernel driven models etc. Then, new methodologies on land surface parameters inversion based on multi-source remote sensing data are proposed, taking the land surface Albedo, leaf area index, temperature/emissivity, and surface net radiation as examples. A new synthetic land surface parameter quantitative remote sensing product generation system is suggested and the software system prototype will be demonstrated. At last, multi-scale field experiment campaigns, such as the field campaigns in Gansu and Beijing, China are introduced briefly. The ground based, tower based, and airborne multi-angular measurement system have been built to measure the directional reflectance, emission and scattering characteristics from visible, near infrared, thermal infrared and microwave bands for model validation and calibration. The remote sensing pixel scale "true value" measurement strategy have been designed to gain the ground "true value" of LST, ALBEDO, LAI, soil moisture and ET etc. at 1-km2 for remote sensing product validation.

Satellite tagging and biopsy sampling of killer whales at subantarctic Marion Island: effectiveness, immediate reactions and long-term responses.

PubMed

Reisinger, Ryan R; Oosthuizen, W Chris; Péron, Guillaume; Cory Toussaint, Dawn; Andrews, Russel D; de Bruyn, P J Nico

2014-01-01

Remote tissue biopsy sampling and satellite tagging are becoming widely used in large marine vertebrate studies because they allow the collection of a diverse suite of otherwise difficult-to-obtain data which are critical in understanding the ecology of these species and to their conservation and management. Researchers must carefully consider their methods not only from an animal welfare perspective, but also to ensure the scientific rigour and validity of their results. We report methods for shore-based, remote biopsy sampling and satellite tagging of killer whales Orcinus orca at Subantarctic Marion Island. The performance of these methods is critically assessed using 1) the attachment duration of low-impact minimally percutaneous satellite tags; 2) the immediate behavioural reactions of animals to biopsy sampling and satellite tagging; 3) the effect of researcher experience on biopsy sampling and satellite tagging; and 4) the mid- (1 month) and long- (24 month) term behavioural consequences. To study mid- and long-term behavioural changes we used multievent capture-recapture models that accommodate imperfect detection and individual heterogeneity. We made 72 biopsy sampling attempts (resulting in 32 tissue samples) and 37 satellite tagging attempts (deploying 19 tags). Biopsy sampling success rates were low (43%), but tagging rates were high with improved tag designs (86%). The improved tags remained attached for 26±14 days (mean ± SD). Individuals most often showed no reaction when attempts missed (66%) and a slight reaction-defined as a slight flinch, slight shake, short acceleration, or immediate dive-when hit (54%). Severe immediate reactions were never observed. Hit or miss and age-sex class were important predictors of the reaction, but the method (tag or biopsy) was unimportant. Multievent trap-dependence modelling revealed considerable variation in individual sighting patterns; however, there were no significant mid- or long-term changes following biopsy sampling or tagging.

Satellite Tagging and Biopsy Sampling of Killer Whales at Subantarctic Marion Island: Effectiveness, Immediate Reactions and Long-Term Responses

PubMed Central

Reisinger, Ryan R.; Oosthuizen, W. Chris; Péron, Guillaume; Cory Toussaint, Dawn; Andrews, Russel D.; de Bruyn, P. J. Nico

2014-01-01

Remote tissue biopsy sampling and satellite tagging are becoming widely used in large marine vertebrate studies because they allow the collection of a diverse suite of otherwise difficult-to-obtain data which are critical in understanding the ecology of these species and to their conservation and management. Researchers must carefully consider their methods not only from an animal welfare perspective, but also to ensure the scientific rigour and validity of their results. We report methods for shore-based, remote biopsy sampling and satellite tagging of killer whales Orcinus orca at Subantarctic Marion Island. The performance of these methods is critically assessed using 1) the attachment duration of low-impact minimally percutaneous satellite tags; 2) the immediate behavioural reactions of animals to biopsy sampling and satellite tagging; 3) the effect of researcher experience on biopsy sampling and satellite tagging; and 4) the mid- (1 month) and long- (24 month) term behavioural consequences. To study mid- and long-term behavioural changes we used multievent capture-recapture models that accommodate imperfect detection and individual heterogeneity. We made 72 biopsy sampling attempts (resulting in 32 tissue samples) and 37 satellite tagging attempts (deploying 19 tags). Biopsy sampling success rates were low (43%), but tagging rates were high with improved tag designs (86%). The improved tags remained attached for 26±14 days (mean ± SD). Individuals most often showed no reaction when attempts missed (66%) and a slight reaction–defined as a slight flinch, slight shake, short acceleration, or immediate dive–when hit (54%). Severe immediate reactions were never observed. Hit or miss and age-sex class were important predictors of the reaction, but the method (tag or biopsy) was unimportant. Multievent trap-dependence modelling revealed considerable variation in individual sighting patterns; however, there were no significant mid- or long-term changes following biopsy sampling or tagging. PMID:25375329

A comparison of cover calculation techniques for relating point-intercept vegetation sampling to remote sensing imagery

USDA-ARS?s Scientific Manuscript database

Accurate and timely spatial predictions of vegetation cover from remote imagery are an important data source for natural resource management. High-quality in situ data are needed to develop and validate these products. Point-intercept sampling techniques are a common method for obtaining quantitativ...

A multicenter, randomized trial on neuroprotection with remote ischemic per-conditioning during acute ischemic stroke: the REmote iSchemic Conditioning in acUtE BRAin INfarction study protocol.

PubMed

Pico, Fernando; Rosso, Charlotte; Meseguer, Elena; Chadenat, Marie-Laure; Cattenoy, Amina; Aegerter, Philippe; Deltour, Sandrine; Yeung, Jennifer; Hosseini, Hassan; Lambert, Yves; Smadja, Didier; Samson, Yves; Amarenco, Pierre

2016-10-01

Rationale Remote ischemic per-conditioning-causing transient limb ischemia to induce ischemic tolerance in other organs-reduces final infarct size in animal stroke models. Aim To evaluate whether remote ischemic per-conditioning during acute ischemic stroke (<6 h) reduces brain infarct size at 24 h. Methods and design This study is being performed in five French hospitals using a prospective randomized open blinded end-point design. Adults with magnetic resonance imaging confirmed ischemic stroke within 6 h of symptom onset and clinical deficit of 5-25 according to National Institutes of Health Stroke Scale will be randomized 1:1 to remote ischemic per-conditioning or control (stratified by center and intravenous fibrinolysis use). Remote ischemic per-conditioning will consist of four cycles of electronic tourniquet inflation (5 min) and deflation (5 min) to a thigh within 6 h of symptom onset. Magnetic resonance imaging is repeated 24 h after stroke onset. Sample size estimates For a difference of 15 cm 3 in brain infarct growth between groups, 200 patients will be included for 5% significance and 80% power. Study outcomes The primary outcome will be the difference in brain infarct growth from baseline to 24 h in the intervention versus control groups (by diffusion-weighted image magnetic resonance imaging). Secondary outcomes include: National Institutes of Health Stroke Scale score absolute difference between baseline and 24 h, three-month modified Rankin score and daily living activities, mortality, and tolerance and side effects of remote ischemic per-conditioning. Discussion The only remote ischemic per-conditioning trial in humans with stroke did not show remote ischemic per-conditioning to be effective. REmote iSchemic Conditioning in acUtE BRAin INfarction, which has important design differences, should provide more information on the use of this intervention in patients with acute ischemic stroke.

Comparing two remote video survey methods for spatial predictions of the distribution and environmental niche suitability of demersal fishes.

PubMed

Galaiduk, Ronen; Radford, Ben T; Wilson, Shaun K; Harvey, Euan S

2017-12-15

Information on habitat associations from survey data, combined with spatial modelling, allow the development of more refined species distribution modelling which may identify areas of high conservation/fisheries value and consequentially improve conservation efforts. Generalised additive models were used to model the probability of occurrence of six focal species after surveys that utilised two remote underwater video sampling methods (i.e. baited and towed video). Models developed for the towed video method had consistently better predictive performance for all but one study species although only three models had a good to fair fit, and the rest were poor fits, highlighting the challenges associated with modelling habitat associations of marine species in highly homogenous, low relief environments. Models based on baited video dataset regularly included large-scale measures of structural complexity, suggesting fish attraction to a single focus point by bait. Conversely, models based on the towed video data often incorporated small-scale measures of habitat complexity and were more likely to reflect true species-habitat relationships. The cost associated with use of the towed video systems for surveying low-relief seascapes was also relatively low providing additional support for considering this method for marine spatial ecological modelling.

[Estimation of desert vegetation coverage based on multi-source remote sensing data].

PubMed

Wan, Hong-Mei; Li, Xia; Dong, Dao-Rui

2012-12-01

Taking the lower reaches of Tarim River in Xinjiang of Northwest China as study areaAbstract: Taking the lower reaches of Tarim River in Xinjiang of Northwest China as study area and based on the ground investigation and the multi-source remote sensing data of different resolutions, the estimation models for desert vegetation coverage were built, with the precisions of different estimation methods and models compared. The results showed that with the increasing spatial resolution of remote sensing data, the precisions of the estimation models increased. The estimation precision of the models based on the high, middle-high, and middle-low resolution remote sensing data was 89.5%, 87.0%, and 84.56%, respectively, and the precisions of the remote sensing models were higher than that of vegetation index method. This study revealed the change patterns of the estimation precision of desert vegetation coverage based on different spatial resolution remote sensing data, and realized the quantitative conversion of the parameters and scales among the high, middle, and low spatial resolution remote sensing data of desert vegetation coverage, which would provide direct evidence for establishing and implementing comprehensive remote sensing monitoring scheme for the ecological restoration in the study area.

Prediction of high-risk areas for visceral leishmaniasis using socioeconomic indicators and remote sensing data

PubMed Central

2014-01-01

Spatial heterogeneity in the incidence of visceral leishmaniasis (VL) is an important aspect to be considered in planning control actions for the disease. The objective of this study was to predict areas at high risk for visceral leishmaniasis (VL) based on socioeconomic indicators and remote sensing data. We applied classification and regression trees to develop and validate prediction models. Performance of the models was assessed by means of sensitivity, specificity and area under the ROC curve. The model developed was able to discriminate 15 subsets of census tracts (CT) with different probabilities of containing CT with high risk of VL occurrence. The model presented, respectively, in the validation and learning samples, sensitivity of 79% and 52%, specificity of 75% and 66%, and area under the ROC curve of 83% and 66%. Considering the complex network of factors involved in the occurrence of VL in urban areas, the results of this study showed that the development of a predictive model for VL might be feasible and useful for guiding interventions against the disease, but it is still a challenge as demonstrated by the unsatisfactory predictive performance of the model developed. PMID:24885128

Remote vacuum or pressure sealing device and method for critical isolated systems

DOEpatents

Brock, James David [Newport News, VA; Keith, Christopher D [Newport News, VA

2012-07-10

A remote vacuum or pressure sealing apparatus and method for making a radiation tolerant, remotely prepared seal that maintains a vacuum or pressure tight seal throughout a wide temperature range. The remote sealing apparatus includes a fixed threaded sealing surface on an isolated system, a gasket, and an insert consisting of a plug with a protruding sample holder. An insert coupling device, provided for inserting samples within the isolated system, includes a threaded fastener for cooperating with the fixed threaded sealing surface on the isolated system. The insert coupling device includes a locating pin for azimuthal orientation, coupling pins, a tooted coaxial socket wrench, and an insert coupling actuator for actuating the coupling pins. The remote aspect of the sealing apparatus maintains the isolation of the system from the user's environment, safely preserving the user and the system from detrimental effect from each respectively.

Experimental philosophy leading to a small scale digital data base of the conterminous United States for designing experiments with remotely sensed data

NASA Technical Reports Server (NTRS)

Labovitz, M. L.; Masuoka, E. J.; Broderick, P. W.; Garman, T. R.; Ludwig, R. W.; Beltran, G. N.; Heyman, P. J.; Hooker, L. K.

1983-01-01

Research using satellite remotely sensed data, even within any single scientific discipline, often lacked a unifying principle or strategy with which to plan or integrate studies conducted over an area so large that exhaustive examination is infeasible, e.g., the U.S.A. However, such a series of studies would seem to be at the heart of what makes satellite remote sensing unique, that is the ability to select for study from among remotely sensed data sets distributed widely over the U.S., over time, where the resources do not exist to examine all of them. Using this philosophical underpinning and the concept of a unifying principle, an operational procedure for developing a sampling strategy and formal testable hypotheses was constructed. The procedure is applicable across disciplines, when the investigator restates the research question in symbolic form, i.e., quantifies it. The procedure is set within the statistical framework of general linear models. The dependent variable is any arbitrary function of remotely sensed data and the independent variables are values or levels of factors which represent regional climatic conditions and/or properties of the Earth's surface. These factors are operationally defined as maps from the U.S. National Atlas (U.S.G.S., 1970). Eighty-five maps from the National Atlas, representing climatic and surface attributes, were automated by point counting at an effective resolution of one observation every 17.6 km (11 miles) yielding 22,505 observations per map. The maps were registered to one another in a two step procedure producing a coarse, then fine scale registration. After registration, the maps were iteratively checked for errors using manual and automated procedures. The error free maps were annotated with identification and legend information and then stored as card images, one map to a file. A sampling design will be accomplished through a regionalization analysis of the National Atlas data base (presently being conducted). From this analysis a map of homogeneous regions of the U.S.A. will be created and samples (LANDSAT scenes) assigned by region.

Accuracy of gap analysis habitat models in predicting physical features for wildlife-habitat associations in the southwest U.S.

USGS Publications Warehouse

Boykin, K.G.; Thompson, B.C.; Propeck-Gray, S.

2010-01-01

Despite widespread and long-standing efforts to model wildlife-habitat associations using remotely sensed and other spatially explicit data, there are relatively few evaluations of the performance of variables included in predictive models relative to actual features on the landscape. As part of the National Gap Analysis Program, we specifically examined physical site features at randomly selected sample locations in the Southwestern U.S. to assess degree of concordance with predicted features used in modeling vertebrate habitat distribution. Our analysis considered hypotheses about relative accuracy with respect to 30 vertebrate species selected to represent the spectrum of habitat generalist to specialist and categorization of site by relative degree of conservation emphasis accorded to the site. Overall comparison of 19 variables observed at 382 sample sites indicated ???60% concordance for 12 variables. Directly measured or observed variables (slope, soil composition, rock outcrop) generally displayed high concordance, while variables that required judgments regarding descriptive categories (aspect, ecological system, landform) were less concordant. There were no differences detected in concordance among taxa groups, degree of specialization or generalization of selected taxa, or land conservation categorization of sample sites with respect to all sites. We found no support for the hypothesis that accuracy of habitat models is inversely related to degree of taxa specialization when model features for a habitat specialist could be more difficult to represent spatially. Likewise, we did not find support for the hypothesis that physical features will be predicted with higher accuracy on lands with greater dedication to biodiversity conservation than on other lands because of relative differences regarding available information. Accuracy generally was similar (>60%) to that observed for land cover mapping at the ecological system level. These patterns demonstrate resilience of gap analysis deductive model processes to the type of remotely sensed or interpreted data used in habitat feature predictions. ?? 2010 Elsevier B.V.

Integrating remote sensing and terrain data in forest fire modeling

NASA Astrophysics Data System (ADS)

Medler, Michael Johns

Forest fire policies are changing. Managers now face conflicting imperatives to re-establish pre-suppression fire regimes, while simultaneously preventing resource destruction. They must, therefore, understand the spatial patterns of fires. Geographers can facilitate this understanding by developing new techniques for mapping fire behavior. This dissertation develops such techniques for mapping recent fires and using these maps to calibrate models of potential fire hazards. In so doing, it features techniques that strive to address the inherent complexity of modeling the combinations of variables found in most ecological systems. Image processing techniques were used to stratify the elements of terrain, slope, elevation, and aspect. These stratification images were used to assure sample placement considered the role of terrain in fire behavior. Examination of multiple stratification images indicated samples were placed representatively across a controlled range of scales. The incorporation of terrain data also improved preliminary fire hazard classification accuracy by 40%, compared with remotely sensed data alone. A Kauth-Thomas transformation (KT) of pre-fire and post-fire Thematic Mapper (TM) remotely sensed data produced brightness, greenness, and wetness images. Image subtraction indicated fire induced change in brightness, greenness, and wetness. Field data guided a fuzzy classification of these change images. Because fuzzy classification can characterize a continuum of a phenomena where discrete classification may produce artificial borders, fuzzy classification was found to offer a range of fire severity information unavailable with discrete classification. These mapped fire patterns were used to calibrate a model of fire hazards for the entire mountain range. Pre-fire TM, and a digital elevation model produced a set of co-registered images. Training statistics were developed from 30 polygons associated with the previously mapped fire severity. Fuzzy classifications of potential burn patterns were produced from these images. Observed field data values were displayed over the hazard imagery to indicate the effectiveness of the model. Areas that burned without suppression during maximum fire severity are predicted best. Areas with widely spaced trees and grassy understory appear to be misrepresented, perhaps as a consequence of inaccuracies in the initial fire mapping.

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.

A hyper-temporal remote sensing protocol for high-resolution mapping of ecological sites

PubMed Central

Karl, Jason W.

2017-01-01

Ecological site classification has emerged as a highly effective land management framework, but its utility at a regional scale has been limited due to the spatial ambiguity of ecological site locations in the U.S. or the absence of ecological site maps in other regions of the world. In response to these shortcomings, this study evaluated the use of hyper-temporal remote sensing (i.e., hundreds of images) for high spatial resolution mapping of ecological sites. We posit that hyper-temporal remote sensing can provide novel insights into the spatial variability of ecological sites by quantifying the temporal response of land surface spectral properties. This temporal response provides a spectral ‘fingerprint’ of the soil-vegetation-climate relationship which is central to the concept of ecological sites. Consequently, the main objective of this study was to predict the spatial distribution of ecological sites in a semi-arid rangeland using a 28-year time series of normalized difference vegetation index from Landsat TM 5 data and modeled using support vector machine classification. Results from this study show that support vector machine classification using hyper-temporal remote sensing imagery was effective in modeling ecological site classes, with a 62% correct classification. These results were compared to Gridded Soil Survey Geographic database and expert delineated maps of ecological sites which had a 51 and 89% correct classification, respectively. An analysis of the effects of ecological state on ecological site misclassifications revealed that sites in degraded states (e.g., shrub-dominated/shrubland and bare/annuals) had a higher rate of misclassification due to their close spectral similarity with other ecological sites. This study identified three important factors that need to be addressed to improve future model predictions: 1) sampling designs need to fully represent the range of both within class (i.e., states) and between class (i.e., ecological sites) spectral variability through time, 2) field sampling protocols that accurately characterize key soil properties (e.g., texture, depth) need to be adopted, and 3) additional environmental covariates (e.g. terrain attributes) need to be evaluated that may help further differentiate sites with similar spectral signals. Finally, the proposed hyper-temporal remote sensing framework may provide a standardized approach to evaluate and test our ecological site concepts through examining differences in vegetation dynamics in response to climatic variability and other drivers of land-use change. Results from this study demonstrate the efficacy of the hyper-temporal remote sensing approach for high resolution mapping of ecological sites, and highlights its utility in terms of reduced cost and time investment relative to traditional manual mapping approaches. PMID:28414731

A hyper-temporal remote sensing protocol for high-resolution mapping of ecological sites.

PubMed

Maynard, Jonathan J; Karl, Jason W

2017-01-01

Ecological site classification has emerged as a highly effective land management framework, but its utility at a regional scale has been limited due to the spatial ambiguity of ecological site locations in the U.S. or the absence of ecological site maps in other regions of the world. In response to these shortcomings, this study evaluated the use of hyper-temporal remote sensing (i.e., hundreds of images) for high spatial resolution mapping of ecological sites. We posit that hyper-temporal remote sensing can provide novel insights into the spatial variability of ecological sites by quantifying the temporal response of land surface spectral properties. This temporal response provides a spectral 'fingerprint' of the soil-vegetation-climate relationship which is central to the concept of ecological sites. Consequently, the main objective of this study was to predict the spatial distribution of ecological sites in a semi-arid rangeland using a 28-year time series of normalized difference vegetation index from Landsat TM 5 data and modeled using support vector machine classification. Results from this study show that support vector machine classification using hyper-temporal remote sensing imagery was effective in modeling ecological site classes, with a 62% correct classification. These results were compared to Gridded Soil Survey Geographic database and expert delineated maps of ecological sites which had a 51 and 89% correct classification, respectively. An analysis of the effects of ecological state on ecological site misclassifications revealed that sites in degraded states (e.g., shrub-dominated/shrubland and bare/annuals) had a higher rate of misclassification due to their close spectral similarity with other ecological sites. This study identified three important factors that need to be addressed to improve future model predictions: 1) sampling designs need to fully represent the range of both within class (i.e., states) and between class (i.e., ecological sites) spectral variability through time, 2) field sampling protocols that accurately characterize key soil properties (e.g., texture, depth) need to be adopted, and 3) additional environmental covariates (e.g. terrain attributes) need to be evaluated that may help further differentiate sites with similar spectral signals. Finally, the proposed hyper-temporal remote sensing framework may provide a standardized approach to evaluate and test our ecological site concepts through examining differences in vegetation dynamics in response to climatic variability and other drivers of land-use change. Results from this study demonstrate the efficacy of the hyper-temporal remote sensing approach for high resolution mapping of ecological sites, and highlights its utility in terms of reduced cost and time investment relative to traditional manual mapping approaches.

Hyperspectral Remote Sensing and Ecological Modeling Research and Education at Mid America Remote Sensing Center (MARC): Field and Laboratory Enhancement

NASA Technical Reports Server (NTRS)

Cetin, Haluk

1999-01-01

The purpose of this project was to establish a new hyperspectral remote sensing laboratory at the Mid-America Remote sensing Center (MARC), dedicated to in situ and laboratory measurements of environmental samples and to the manipulation, analysis, and storage of remotely sensed data for environmental monitoring and research in ecological modeling using hyperspectral remote sensing at MARC, one of three research facilities of the Center of Reservoir Research at Murray State University (MSU), a Kentucky Commonwealth Center of Excellence. The equipment purchased, a FieldSpec FR portable spectroradiometer and peripherals, and ENVI hyperspectral data processing software, allowed MARC to provide hands-on experience, education, and training for the students of the Department of Geosciences in quantitative remote sensing using hyperspectral data, Geographic Information System (GIS), digital image processing (DIP), computer, geological and geophysical mapping; to provide field support to the researchers and students collecting in situ and laboratory measurements of environmental data; to create a spectral library of the cover types and to establish a World Wide Web server to provide the spectral library to other academic, state and Federal institutions. Much of the research will soon be published in scientific journals. A World Wide Web page has been created at the web site of MARC. Results of this project are grouped in two categories, education and research accomplishments. The Principal Investigator (PI) modified remote sensing and DIP courses to introduce students to ii situ field spectra and laboratory remote sensing studies for environmental monitoring in the region by using the new equipment in the courses. The PI collected in situ measurements using the spectroradiometer for the ER-2 mission to Puerto Rico project for the Moderate Resolution Imaging Spectrometer (MODIS) Airborne Simulator (MAS). Currently MARC is mapping water quality in Kentucky Lake and vegetation in the Land-Between-the Lakes (LBL) using Landsat-TM data. A Landsat-TM scene of the same day was obtained to relate ground measurements to the satellite data. A spectral library has been created for overstory species in LBL. Some of the methods, such as NPDF and IDFD techniques for spectral unmixing and reduction of effects of shadows in classifications- comparison of hyperspectral classification techniques, and spectral nonlinear and linear unmixing techniques, are being tested using the laboratory.

A versatile system for biological and soil chemical tests on a planetary landing craft. II - Hardware development

NASA Technical Reports Server (NTRS)

Martin, J. P.; Kok, B.; Radmer, R.

1976-01-01

A system has been under development which is designed to seek remotely for clues to life in planetary soil samples. The basic approach is a set of experiments, all having a common sensor, a gas analysis mass spectrometer which monitors gas composition in the head spaces above sealed, temperature controlled soil samples. Versatility is obtained with up to three preloaded, sealed fluid injector capsules for each of eleven soil test cells. Tests results with an engineering model has demonstrated performance capability of subsystem components such as soil distribution, gas sampling valves, injector mechanisms, temperature control, and test cell seal.

Digital soil mapping using remote sensing indices, terrain attributes, and vegetation features in the rangelands of northeastern Iran.

PubMed

Mahmoudabadi, Ebrahim; Karimi, Alireza; Haghnia, Gholam Hosain; Sepehr, Adel

2017-09-11

Digital soil mapping has been introduced as a viable alternative to the traditional mapping methods due to being fast and cost-effective. The objective of the present study was to investigate the capability of the vegetation features and spectral indices as auxiliary variables in digital soil mapping models to predict soil properties. A region with an area of 1225 ha located in Bajgiran rangelands, Khorasan Razavi province, northeastern Iran, was chosen. A total of 137 sampling sites, each containing 3-5 plots with 10-m interval distance along a transect established based on randomized-systematic method, were investigated. In each plot, plant species names and numbers as well as vegetation cover percentage (VCP) were recorded, and finally one composite soil sample was taken from each transect at each site (137 soil samples in total). Terrain attributes were derived from a digital elevation model, different bands and spectral indices were obtained from the Landsat7 ETM+ images, and vegetation features were calculated in the plots, all of which were used as auxiliary variables to predict soil properties using artificial neural network, gene expression programming, and multivariate linear regression models. According to R 2 RMSE and MBE values, artificial neutral network was obtained as the most accurate soil properties prediction function used in scorpan model. Vegetation features and indices were more effective than remotely sensed data and terrain attributes in predicting soil properties including calcium carbonate equivalent, clay, bulk density, total nitrogen, carbon, sand, silt, and saturated moisture capacity. It was also shown that vegetation indices including NDVI, SAVI, MSAVI, SARVI, RDVI, and DVI were more effective in estimating the majority of soil properties compared to separate bands and even some soil spectral indices.

Modeling light scattering by mineral dust particles using spheroids

NASA Astrophysics Data System (ADS)

Merikallio, Sini; Nousiainen, Timo

Suspended dust particles have a considerable influence on light scattering in both terrestrial and planetary atmospheres and can therefore have a large effect on the interpretation of remote sensing measurements. Assuming dust particles to be spherical is known to produce inaccurate results when modeling optical properties of real mineral dust particles. Yet this approximation is widely used for its simplicity. Here, we simulate light scattering by mineral dust particles using a distribution of model spheroids. This is done by comparing scattering matrices calculated from a dust optical database of Dubovik et al. [2006] with those measured in the laboratory by Volten et al. [2001]. Wavelengths of 441,6 nm and 632,8 nm and refractive indexes of Re = 1.55 -1.7 and Im = 0.001i -0.01i were adopted in this study. Overall, spheroids are found to fit the measurements significantly better than Mie spheres. Further, we confirm that the shape distribution parametrization developed in Nousiainen et al. (2006) significantly improves the accuracy of simulated single-scattering for small mineral dust particles. The spheroid scheme should therefore yield more reliable interpretations of remote sensing data from dusty planetary atmospheres. While the spheroidal scheme is superior to spheres in remote sensing applications, its performance is far from perfect especially for samples with large particles. Thus, additional advances are clearly possible. Further studies of the Martian atmosphere are currently under way. Dubovik et al. (2006) Application of spheroid models to account for aerosol particle nonspheric-ity in remote sensing of desert dust, JGR, Vol. 111, D11208 Volten et al. (2001) Scattering matrices of mineral aerosol particles at 441.6 nm and 632.8 nm, JGR, Vol. 106, No. D15, pp. 17375-17401 Nousiainen et al. (2006) Light scattering modeling of small feldspar aerosol particles using polyhedral prisms and spheroids, JQSRT 101, pp. 471-487

Quantifying uncertainties in radar forward models through a comparison between CloudSat and SPartICus reflectivity factors

NASA Astrophysics Data System (ADS)

Mascio, Jeana; Mace, Gerald G.

2017-02-01

Interpretations of remote sensing measurements collected in sample volumes containing ice-phase hydrometeors are very sensitive to assumptions regarding the distributions of mass with ice crystal dimension, otherwise known as mass-dimensional or m-D relationships. How these microphysical characteristics vary in nature is highly uncertain, resulting in significant uncertainty in algorithms that attempt to derive bulk microphysical properties from remote sensing measurements. This uncertainty extends to radar reflectivity factors forward calculated from model output because the statistics of the actual m-D in nature is not known. To investigate the variability in m-D relationships in cirrus clouds, reflectivity factors measured by CloudSat are combined with particle size distributions (PSDs) collected by coincident in situ aircraft by using an optimal estimation-based (OE) retrieval of the m-D power law. The PSDs were collected by 12 flights of the Stratton Park Engineering Company Learjet during the Small Particles in Cirrus campaign. We find that no specific habit emerges as preferred, and instead, we find that the microphysical characteristics of ice crystal populations tend to be distributed over a continuum-defying simple categorization. With the uncertainties derived from the OE algorithm, the uncertainties in forward-modeled backscatter cross section and, in turn, radar reflectivity is calculated by using a bootstrapping technique, allowing us to infer the uncertainties in forward-modeled radar reflectivity that would be appropriately applied to remote sensing simulator algorithms.

What are the fluxes of greenhouse gases from the greater Los Angeles area as inferred from top-down remote sensing studies?

NASA Astrophysics Data System (ADS)

Hedelius, J.; Wennberg, P. O.; Wunch, D.; Roehl, C. M.; Podolske, J. R.; Hillyard, P.; Iraci, L. T.

2017-12-01

Greenhouse gas (GHG) emissions from California's South Coast Air Basin (SoCAB) have been studied extensively using a variety of tower, aircraft, remote sensing, emission inventory, and modeling studies. It is impractical to survey GHG fluxes from all urban areas and hot-spots to the extent the SoCAB has been studied, but it can serve as a test location for scaling methods globally. We use a combination of remote sensing measurements from ground (Total Carbon Column Observing Network, TCCON) and space-based (Observing Carbon Observatory-2, OCO-2) sensors in an inversion to obtain the carbon dioxide flux from the SoCAB. We also perform a variety of sensitivity tests to see how the inversion performs using different model parameterizations. Fluxes do not significantly depend on the mixed layer depth, but are sensitive to the model surface layers (<5 m). Carbon dioxide fluxes are larger than those from bottom-up inventories by about 20%, and along with CO has a significant weekend:weekday effect. Methane fluxes have little weekend changes. Results also include flux estimates from sub-regions of the SoCAB. Larger top-down than bottom-up fluxes highlight the need for additional work on both approaches. Higher top-down fluxes could arise from sampling bias, model bias, or may show bottom-up values underestimate sources. Lessons learned here may help in scaling up inversions to hundreds of urban systems using space-based observations.

Purification of Training Samples Based on Spectral Feature and Superpixel Segmentation

NASA Astrophysics Data System (ADS)

Guan, X.; Qi, W.; He, J.; Wen, Q.; Chen, T.; Wang, Z.

2018-04-01

Remote sensing image classification is an effective way to extract information from large volumes of high-spatial resolution remote sensing images. Generally, supervised image classification relies on abundant and high-precision training data, which is often manually interpreted by human experts to provide ground truth for training and evaluating the performance of the classifier. Remote sensing enterprises accumulated lots of manually interpreted products from early lower-spatial resolution remote sensing images by executing their routine research and business programs. However, these manually interpreted products may not match the very high resolution (VHR) image properly because of different dates or spatial resolution of both data, thus, hindering suitability of manually interpreted products in training classification models, or small coverage area of these manually interpreted products. We also face similar problems in our laboratory in 21st Century Aerospace Technology Co. Ltd (short for 21AT). In this work, we propose a method to purify the interpreted product to match newly available VHRI data and provide the best training data for supervised image classifiers in VHR image classification. And results indicate that our proposed method can efficiently purify the input data for future machine learning use.

Improving Global Models of Remotely Sensed Ocean Chlorophyll Content Using Partial Least Squares and Geographically Weighted Regression

NASA Astrophysics Data System (ADS)

Gholizadeh, H.; Robeson, S. M.

2015-12-01

Empirical models have been widely used to estimate global chlorophyll content from remotely sensed data. Here, we focus on the standard NASA empirical models that use blue-green band ratios. These band ratio ocean color (OC) algorithms are in the form of fourth-order polynomials and the parameters of these polynomials (i.e. coefficients) are estimated from the NASA bio-Optical Marine Algorithm Data set (NOMAD). Most of the points in this data set have been sampled from tropical and temperate regions. However, polynomial coefficients obtained from this data set are used to estimate chlorophyll content in all ocean regions with different properties such as sea-surface temperature, salinity, and downwelling/upwelling patterns. Further, the polynomial terms in these models are highly correlated. In sum, the limitations of these empirical models are as follows: 1) the independent variables within the empirical models, in their current form, are correlated (multicollinear), and 2) current algorithms are global approaches and are based on the spatial stationarity assumption, so they are independent of location. Multicollinearity problem is resolved by using partial least squares (PLS). PLS, which transforms the data into a set of independent components, can be considered as a combined form of principal component regression (PCR) and multiple regression. Geographically weighted regression (GWR) is also used to investigate the validity of spatial stationarity assumption. GWR solves a regression model over each sample point by using the observations within its neighbourhood. PLS results show that the empirical method underestimates chlorophyll content in high latitudes, including the Southern Ocean region, when compared to PLS (see Figure 1). Cluster analysis of GWR coefficients also shows that the spatial stationarity assumption in empirical models is not likely a valid assumption.

Integrating field plots, lidar, and landsat time series to provide temporally consistent annual estimates of biomass from 1990 to present

Treesearch

Warren B. Cohen; Hans-Erik Andersen; Sean P. Healey; Gretchen G. Moisen; Todd A. Schroeder; Christopher W. Woodall; Grant M. Domke; Zhiqiang Yang; Robert E. Kennedy; Stephen V. Stehman; Curtis Woodcock; Jim Vogelmann; Zhe Zhu; Chengquan Huang

2015-01-01

We are developing a system that provides temporally consistent biomass estimates for national greenhouse gas inventory reporting to the United Nations Framework Convention on Climate Change. Our model-assisted estimation framework relies on remote sensing to scale from plot measurements to lidar strip samples, to Landsat time series-based maps. As a demonstration, new...

Description of European Space Agency (ESA) Remote Manipulator (RM) System Breadboard Currently Under Development for Demonstration of Critical Technology Foreseen to be Used in the Mars Sample Receiving Facility (MSRF)

NASA Astrophysics Data System (ADS)

Vrublevskis, J.; Duncan, S.; Berthoud, L.; Bowman, P.; Hills, R.; McCulloch, Y.; Pisla, D.; Vaida, C.; Gherman, B.; Hofbaur, M.; Dieber, B.; Neythalath, N.; Smith, C.; van Winnendael, M.; Duvet, L.

2018-04-01

In order to avoid the use of 'double walled' gloves, a haptic feedback Remote Manipulation (RM) system rather than a gloved isolator is needed inside a Double Walled Isolator (DWI) to handle a sample returned from Mars.

Classification accuracy for stratification with remotely sensed data

Treesearch

Raymond L. Czaplewski; Paul L. Patterson

2003-01-01

Tools are developed that help specify the classification accuracy required from remotely sensed data. These tools are applied during the planning stage of a sample survey that will use poststratification, prestratification with proportional allocation, or double sampling for stratification. Accuracy standards are developed in terms of an “error matrix,” which is...

Mobile inductively coupled plasma system

DOEpatents

D'Silva, Arthur P.; Jaselskis, Edward J.

1999-03-30

A system for sampling and analyzing a material located at a hazardous site. A laser located remote from the hazardous site is connected to an optical fiber, which directs laser radiation proximate the material at the hazardous site. The laser radiation abates a sample of the material. An inductively coupled plasma is located remotely from the material. An aerosol transport system carries the ablated particles to a plasma, where they are dissociated, atomized and excited to provide characteristic optical reduction of the elemental constituents of the sample. An optical spectrometer is located remotely from the site. A second optical fiber is connected to the optical spectrometer at one end and the plasma source at the other end to carry the optical radiation from the plasma source to the spectrometer.

A Robust False Matching Points Detection Method for Remote Sensing Image Registration

NASA Astrophysics Data System (ADS)

Shan, X. J.; Tang, P.

2015-04-01

Given the influences of illumination, imaging angle, and geometric distortion, among others, false matching points still occur in all image registration algorithms. Therefore, false matching points detection is an important step in remote sensing image registration. Random Sample Consensus (RANSAC) is typically used to detect false matching points. However, RANSAC method cannot detect all false matching points in some remote sensing images. Therefore, a robust false matching points detection method based on Knearest- neighbour (K-NN) graph (KGD) is proposed in this method to obtain robust and high accuracy result. The KGD method starts with the construction of the K-NN graph in one image. K-NN graph can be first generated for each matching points and its K nearest matching points. Local transformation model for each matching point is then obtained by using its K nearest matching points. The error of each matching point is computed by using its transformation model. Last, L matching points with largest error are identified false matching points and removed. This process is iterative until all errors are smaller than the given threshold. In addition, KGD method can be used in combination with other methods, such as RANSAC. Several remote sensing images with different resolutions and terrains are used in the experiment. We evaluate the performance of KGD method, RANSAC + KGD method, RANSAC, and Graph Transformation Matching (GTM). The experimental results demonstrate the superior performance of the KGD and RANSAC + KGD methods.

A study of remote sensing as applied to regional and small watersheds. Volume 1: Summary report

NASA Technical Reports Server (NTRS)

Ambaruch, R.

1974-01-01

The accuracy of remotely sensed measurements to provide inputs to hydrologic models of watersheds is studied. A series of sensitivity analyses on continuous simulation models of three watersheds determined: (1)Optimal values and permissible tolerances of inputs to achieve accurate simulation of streamflow from the watersheds; (2) Which model inputs can be quantified from remote sensing, directly, indirectly or by inference; and (3) How accurate remotely sensed measurements (from spacecraft or aircraft) must be to provide a basis for quantifying model inputs within permissible tolerances.

Sampling strategies on Mars: Remote and not-so-remote observations from a surface rover

NASA Technical Reports Server (NTRS)

Singer, R. B.

1988-01-01

The mobility and speed of a semi-autonomous Mars rover are of necessity limited by the need to think and stay out of trouble. This consideration makes it essential that the rover's travels be carefully directed to likely targets of interest for sampling and in situ study. Short range remote sensing conducted from the rover, based on existing technology, can provide significant information about the chemistry and mineralogy of surrounding rocks and soils in support of sampling efforts. These observations are of course of direct scientific importance as well. Because of the small number of samples actually to be returned to Earth, it is also important that candidate samples be analyzed aboard the rover so that diversity can be maximized. It is essential to perform certain types of analyses, such as those involving volatiles, prior to the thermal and physical shocks of the return trip to Earth. In addition, whatever measurements can be made of nonreturned samples will be important to enlarge the context of the detailed analyses to be performed later on the few returned samples. Some considerations related to these objectives are discussed.

Using empirical orthogonal functions from remote sensing reflectance spectra to predict various phytoplankton pigment concentrations in the Eastern Tropical Atlantic

NASA Astrophysics Data System (ADS)

Bracher, Astrid; Taylor, Bettina; Taylor, Marc; Steinmetz, Francois; Dinter, Tilman; Röttgers, Rüdiger

2014-05-01

Phytoplankton pigments play a major role in photosynthesis and photoprotection. Their composition and abundance give information on characteristics of a phytoplankton community in respect to its acclimation to light, overall biomass and composition of major phytoplankton groups. Most phytoplankton pigments can be measured by applying HPLC techniques to filtered water samples. This method like other mathods analysing water samples in the laboratory is time consuming and therefore only a limited number of samples can be obtained. In order to obtain information on phytoplankton pigment composition with a better temporal and spatial composition, the rationale was to develop a method to get from continuous optical measurements pigment concentrations. We have used remote sensing reflectances (RRS) derived from ship-based hyper-spectral underwater radiometric and from satellite MERIS measurements (using the POLYMER algorithm developed by Steinmetz et al. 2011), sampled in the Eastern Tropical Atlantic, to predict the water surface concentration of various pigments or pigment groups in this area. A statistical model based on Empirical Orthogonal Function (EOF) analysis of these RRS spectra was developed. Then subsequently linear models with measured (collocated) pigment concentrations as the response variable and EOF loadings as predictor variables were constructed. The model results, which have been verified by cross validation, show that from the ship-based RRS measurements the surface concentrations of a suite of pigments and pigment groups can be well predicted, even when only a multi-spectral resolution of RRS data is chosen. Based on the MERIS reflectance data, only concentrations of total chlorophyll-a (chl-a), monovinyl-chl-a and the groups of photoprotective and photosynthetic carotenoids can be obtained with high quality. The model constructed on the satellite reflectances as input was also applied to one month of MERIS POLYMER data to predict for the whole Eastern Tropical Atlantic area the concentration of those pigments. Finally, the potential, limitations and future perspectives for the application of our generic method are discussed.

Relation of laboratory and remotely sensed spectral signatures of ocean-dumped acid waste

NASA Technical Reports Server (NTRS)

Lewis, B. W.

1978-01-01

Results of laboratory transmission and remotely sensed ocean upwelled spectral signatures of acid waste ocean water solutions are presented. The studies were performed to establish ocean-dumped acid waste spectral signatures and to relate them to chemical and physical interactions occurring in the dump plume. The remotely sensed field measurements and the laboratory measurements were made using the same rapid-scanning spectrometer viewing a dump plume and with actual acid waste and ocean water samples, respectively. Laboratory studies showed that the signatures were produced by soluble ferric iron being precipitated in situ as ferric hydroxide upon dilution with ocean water. Sea-truth water samples were taken and analyzed for pertinent major components of the acid waste. Relationships were developed between the field and laboratory data both for spectral signatures and color changes with concentration. The relationships allow for the estimation of concentration of the indicator iron from remotely sensed spectral data and the laboratory transmission concentration data without sea-truth samples.

Pb isotopes as an indicator of the Asian contribution to particulate air pollution in urban California.

PubMed

Ewing, Stephanie A; Christensen, John N; Brown, Shaun T; Vancuren, Richard A; Cliff, Steven S; Depaolo, Donald J

2010-12-01

During the last two decades, expanding industrial activity in east Asia has led to increased production of airborne pollutants that can be transported to North America. Previous efforts to detect this trans-Pacific pollution have relied upon remote sensing and remote sample locations. We tested whether Pb isotope ratios in airborne particles can be used to directly evaluate the Asian contribution to airborne particles of anthropogenic origin in western North America, using a time series of samples from a pair of sites upwind and downwind of the San Francisco Bay Area. Our results for airborne Pb at these sites indicate a median value of 29% Asian origin, based on mixing relations between distinct regional sample groups. This trans-Pacific Pb is present in small quantities but serves as a tracer for airborne particles within the growing Asian industrial plume. We then applied this analysis to archived samples from urban sites in central California. Taken together, our results suggest that the analysis of Pb isotopes can reveal the distribution of airborne particles affected by Asian industrial pollution at urban sites in northern California. Under suitable circumstances, this analysis can improve understanding of the global transport of pollution, independent of transport models.

Pb Isotopes as an Indicator of the Asian Contribution to Particulate Air Pollution in Urban California

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

Ewing, Stephanie A.; Christensen, John N.; Brown, Shaun T.

2010-10-25

During the last two decades, expanding industrial activity in east Asia has led to increased production of airborne pollutants that can be transported to North America. Previous efforts to detect this trans-Pacific pollution have relied upon remote sensing and remote sample locations. We tested whether Pb isotope ratios in airborne particles can be used to directly evaluate the Asian contribution to airborne particles of anthropogenic origin in western North America, using a time series of samples from a pair of sites upwind and downwind of the San Francisco Bay Area. Our results for airborne Pb at these sites indicate amore » median value of 29 Asian origin, based on mixing relations between distinct regional sample groups. This trans-Pacific Pb is present in small quantities but serves as a tracer for airborne particles within the growing Asian industrial plume. We then applied this analysis to archived samples from urban sites in central California. Taken together, our results suggest that the analysis of Pb isotopes can reveal the distribution of airborne particles affected by Asian industrial pollution at urban sites in northern California. Under suitable circumstances, this analysis can improve understanding of the global transport of pollution, independent of transport models.« less

Determining phytoplankton community structure from ocean color at the Martha's Vineyard Coastal Observatory (MVCO)

NASA Astrophysics Data System (ADS)

Kramer, S. J.; Sosik, H. M.; Roesler, C. S.

2016-02-01

Satellite remote sensing of ocean color allows for estimates of phytoplankton biomass on broad spatial and temporal scales. Recently, a variety of approaches have been offered for determining phytoplankton taxonomic composition or phytoplankton functional types (PFTs) from remote sensing reflectance. These bio-optical algorithms exploit spectral differences to discriminate waters dominated by different types of cells. However, the efficacy of these models remains difficult to constrain due to limited datasets for detailed validation. In this study, we examined the region around the Martha's Vineyard Coastal Observatory (MVCO), a near-shore location on the New England shelf with optically complex coastal waters. This site offers many methods for detailed validation of ocean color algorithms: an AERONET-OC above-water radiometry system provides sea-truth ocean color observations; time series of absorption and backscattering coefficients are measured; and phytoplankton composition is assessed with a combination of continuous in situ flow cytometry and intermittent discrete sampling for HPLC pigments. Our analysis showed that even models originally parameterized for the Northwest Atlantic perform poorly in capturing the variability in relationships between optical properties and water constituents at coastal sites such as MVCO. We refined models with local parameterizations of variability in absorption and backscattering coefficients, and achieved much better agreement of modeled and observed relationships between predicted spectral reflectance, chlorophyll concentration, and indices of phytoplankton composition such as diatom dominance. Applying these refined models to satellite remote sensing imagery offers the possibility of describing large-scale variations in phytoplankton community structure both at MVCO and on the surrounding shelf over space and time.

New Statistical Model for Variability of Aerosol Optical Thickness: Theory and Application to MODIS Data over Ocean

NASA Technical Reports Server (NTRS)

Alexandrov, Mikhail Dmitrievic; Geogdzhayev, Igor V.; Tsigaridis, Konstantinos; Marshak, Alexander; Levy, Robert; Cairns, Brian

2016-01-01

A novel model for the variability in aerosol optical thickness (AOT) is presented. This model is based on the consideration of AOT fields as realizations of a stochastic process, that is the exponent of an underlying Gaussian process with a specific autocorrelation function. In this approach AOT fields have lognormal PDFs and structure functions having the correct asymptotic behavior at large scales. The latter is an advantage compared with fractal (scale-invariant) approaches. The simple analytical form of the structure function in the proposed model facilitates its use for the parameterization of AOT statistics derived from remote sensing data. The new approach is illustrated using a month-long global MODIS AOT dataset (over ocean) with 10 km resolution. It was used to compute AOT statistics for sample cells forming a grid with 5deg spacing. The observed shapes of the structure functions indicated that in a large number of cases the AOT variability is split into two regimes that exhibit different patterns of behavior: small-scale stationary processes and trends reflecting variations at larger scales. The small-scale patterns are suggested to be generated by local aerosols within the marine boundary layer, while the large-scale trends are indicative of elevated aerosols transported from remote continental sources. This assumption is evaluated by comparison of the geographical distributions of these patterns derived from MODIS data with those obtained from the GISS GCM. This study shows considerable potential to enhance comparisons between remote sensing datasets and climate models beyond regional mean AOTs.

Working Around Cosmic Variance: Remote Quadrupole Measurements of the CMB

NASA Astrophysics Data System (ADS)

Adil, Arsalan; Bunn, Emory

2018-01-01

Anisotropies in the CMB maps continue to revolutionize our understanding of the Cosmos. However, the statistical interpretation of these anisotropies is tainted with a posteriori statistics. The problem is particularly emphasized for lower order multipoles, i.e. in the cosmic variance regime of the power spectrum. Naturally, the solution lies in acquiring a new data set – a rather difficult task given the sample size of the Universe.The CMB temperature, in theory, depends on: the direction of photon propagation, the time at which the photons are observed, and the observer’s location in space. In existing CMB data, only the first parameter varies. However, as first pointed out by Kamionkowski and Loeb, a solution lies in making the so-called “Remote Quadrupole Measurements” by analyzing the secondary polarization produced by incoming CMB photons via the Sunyaev-Zel’dovich (SZ) effect. These observations allow us to measure the projected CMB quadrupole at the location and look-back time of a galaxy cluster.At low redshifts, the remote quadrupole is strongly correlated to the CMB anisotropy from our last scattering surface. We provide here a formalism for computing the covariance and relation matrices for both the two-point correlation function on the last scattering surface of a galaxy cluster and the cross correlation of the remote quadrupole with the local CMB. We then calculate these matrices based on a fiducial model and a non-standard model that suppresses power at large angles for ~104 clusters up to z=2. We anticipate to make a priori predictions of the differences between our expectations for the standard and non-standard models. Such an analysis is timely in the wake of the CMB S4 era which will provide us with an extensive SZ cluster catalogue.

Utility of BRDF Models for Estimating Optimal View Angles in Classification of Remotely Sensed Images

NASA Technical Reports Server (NTRS)

Valdez, P. F.; Donohoe, G. W.

1997-01-01

Statistical classification of remotely sensed images attempts to discriminate between surface cover types on the basis of the spectral response recorded by a sensor. It is well known that surfaces reflect incident radiation as a function of wavelength producing a spectral signature specific to the material under investigation. Multispectral and hyperspectral sensors sample the spectral response over tens and even hundreds of wavelength bands to capture the variation of spectral response with wavelength. Classification algorithms then exploit these differences in spectral response to distinguish between materials of interest. Sensors of this type, however, collect detailed spectral information from one direction (usually nadir); consequently, do not consider the directional nature of reflectance potentially detectable at different sensor view angles. Improvements in sensor technology have resulted in remote sensing platforms capable of detecting reflected energy across wavelengths (spectral signatures) and from multiple view angles (angular signatures) in the fore and aft directions. Sensors of this type include: the moderate resolution imaging spectroradiometer (MODIS), the multiangle imaging spectroradiometer (MISR), and the airborne solid-state array spectroradiometer (ASAS). A goal of this paper, then, is to explore the utility of Bidirectional Reflectance Distribution Function (BRDF) models in the selection of optimal view angles for the classification of remotely sensed images by employing a strategy of searching for the maximum difference between surface BRDFs. After a brief discussion of directional reflect ante in Section 2, attention is directed to the Beard-Maxwell BRDF model and its use in predicting the bidirectional reflectance of a surface. The selection of optimal viewing angles is addressed in Section 3, followed by conclusions and future work in Section 4.

Measurement of Persistent Organic Pollutants (POPs) in plastic resin pellets from remote islands : Toward establishment of baseline level for International Pellet Watch

NASA Astrophysics Data System (ADS)

Takada, H.; Heskett, M.; Yamashita, R.; Yuyama, M.; Itoh, M.; Geok, Y. B.; Ogata, Y.

2011-12-01

Plastic resin pellets collected from remote islands in open oceans (Canary, St. Helena, Cocos, Hawaii, Maui Islands and Barbados) were sorted and yellowing polyethylene (PE) pellets were measured for polychlorinated biphenyls (PCBs), dichlorodiphenyltrichloroethane and the degradation products (DDTs), and hexachlorocyclohexanes (HCHs) by gas chromatograph equipped with mass spectrometer (GC-MS) and with electron capture detector (GC-ECD). PCBs were detected from all the pellet samples, confirming the global dispersion of PCBs. Median concentrations of PCBs (sum of 13 congeners : CB-66, CB-101, CB-110, CB-118, CB-105, CB-149, CB-153, CB-138, CB-128, CB-187, CB-180, CB-170, CB-206) in the remote island pellets ranged from 0.1 to 10 ng/g-pellet. These were one to three orders of magnitude lower than those observed for pellets from industrialized coastal zones (hundreds ng/g in Los Angeles, Boston, Tokyo; Ogata et al., 2009). Because these remote islands are far (>100 km) from industrialized zones, these concentrations (i.e., 0.1 to 10 ng/g-pellet) can be regarded as global "baseline" level of PCB pollution. Concentrations of DDTs in the remote island pellets ranged from 0.2 to 5.5 ng/g-pellet. At some locations, DDT was dominant over the degradation products (DDE and DDD), suggesting current usage of the pesticides in the islands. HCHs concentrations were 0.4 - 1.8 ng/g-pellet and lower than PCBs and DDTs, except for St. Helena Island at 18.8 ng/g-pellet where the current usage of the pesticides are of concern. The analyses of pellets from the remote islands provided "baseline" level of POPs (PCBs < 10 ng/g-pellet, DDTs < 6 ng/g-pellet, HCHs < 2 ng/g-pellet). However, the present samples were from tropical and subtropical areas. To establish global baseline, especially to understand the effects of global distillation, pellet samples from remote islands in higher latitude regions are necessary. From the eco-toxicological point of view, the fact that sporadic high concentrations of POPs were detected in some pellet samples from the remote islands is underscored. Some plastic debris which were contaminated in industrialized coastal zones may have rapidly transported to the remote islands before they would reach equilibrium (i.e., desorption completed). Because POPs concentrations in the other media are at trace levels in these remote environments, the sporadic high concentrations of POPs in the plastic debris may pose threat to the ecosystem in the remote islands.

DARLA: Data Assimilation and Remote Sensing for Littoral Applications

NASA Astrophysics Data System (ADS)

Jessup, A.; Holman, R. A.; Chickadel, C.; Elgar, S.; Farquharson, G.; Haller, M. C.; Kurapov, A. L.; Özkan-Haller, H. T.; Raubenheimer, B.; Thomson, J. M.

2012-12-01

DARLA is 5-year collaborative project that couples state-of-the-art remote sensing and in situ measurements with advanced data assimilation (DA) modeling to (a) evaluate and improve remote sensing retrieval algorithms for environmental parameters, (b) determine the extent to which remote sensing data can be used in place of in situ data in models, and (c) infer bathymetry for littoral environments by combining remotely-sensed parameters and data assimilation models. The project uses microwave, electro-optical, and infrared techniques to characterize the littoral ocean with a focus on wave and current parameters required for DA modeling. In conjunction with the RIVET (River and Inlets) Project, extensive in situ measurements provide ground truth for both the remote sensing retrieval algorithms and the DA modeling. Our goal is to use remote sensing to constrain data assimilation models of wave and circulation dynamics in a tidal inlet and surrounding beaches. We seek to improve environmental parameter estimation via remote sensing fusion, determine the success of using remote sensing data to drive DA models, and produce a dynamically consistent representation of the wave, circulation, and bathymetry fields in complex environments. The objectives are to test the following three hypotheses: 1. Environmental parameter estimation using remote sensing techniques can be significantly improved by fusion of multiple sensor products. 2. Data assimilation models can be adequately constrained (i.e., forced or guided) with environmental parameters derived from remote sensing measurements. 3. Bathymetry on open beaches, river mouths, and at tidal inlets can be inferred from a combination of remotely-sensed parameters and data assimilation models. Our approach is to conduct a series of field experiments combining remote sensing and in situ measurements to investigate signature physics and to gather data for developing and testing DA models. A preliminary experiment conducted at the Field Research Facility at Duck, NC in September 2010 focused on assimilation of tower-based electo-optical, infrared, and radar measurements in predictions of longshore currents. Here we provide an overview of our contribution to the RIVET I experiment at New River Inlet, NC in May 2012. During the course of the 3-week measurement period, continuous tower-based remote sensing measurements were made using electro-optical, infrared, and radar techniques covering the nearshore zone and the inlet mouth. A total of 50 hours of airborne measurements were made using high-resolution infrared imagers and a customized along track interferometric synthetic aperture radar (ATI SAR). The airborne IR imagery provides kilometer-scale mapping of frontal features that evolve as the inlet flow interacts with the oceanic wave and current fields. The ATI SAR provides maps of the two-dimensional surface currents. Near-surface measurements of turbulent velocities and surface waves using SWIFT drifters, designed to measures near-surface properties relevant to remote sensing, complimented the extensive in situ measurements by RIVET investigators.

Comparing live and remote models in eating conformity research.

PubMed

Feeney, Justin R; Polivy, Janet; Pliner, Patricia; Sullivan, Margot D

2011-01-01

Research demonstrates that people conform to how much other people eat. This conformity occurs in the presence of other people (live model) and when people view information about how much food prior participants ate (remote models). The assumption in the literature has been that remote models produce a similar effect to live models, but this has never been tested. To investigate this issue, we randomly paired participants with a live or remote model and compared their eating to those who ate alone. We found that participants exposed to both types of model differed significantly from those in the control group, but there was no significant difference between the two modeling procedures. Crown Copyright © 2010. Published by Elsevier Ltd. All rights reserved.

Time Series Remote Sensing in Monitoring the Spatio-Temporal Dynamics of Plant Invasions: A Study of Invasive Saltcedar (Tamarix Spp.)

NASA Astrophysics Data System (ADS)

Diao, Chunyuan

In today's big data era, the increasing availability of satellite and airborne platforms at various spatial and temporal scales creates unprecedented opportunities to understand the complex and dynamic systems (e.g., plant invasion). Time series remote sensing is becoming more and more important to monitor the earth system dynamics and interactions. To date, most of the time series remote sensing studies have been conducted with the images acquired at coarse spatial scale, due to their relatively high temporal resolution. The construction of time series at fine spatial scale, however, is limited to few or discrete images acquired within or across years. The objective of this research is to advance the time series remote sensing at fine spatial scale, particularly to shift from discrete time series remote sensing to continuous time series remote sensing. The objective will be achieved through the following aims: 1) Advance intra-annual time series remote sensing under the pure-pixel assumption; 2) Advance intra-annual time series remote sensing under the mixed-pixel assumption; 3) Advance inter-annual time series remote sensing in monitoring the land surface dynamics; and 4) Advance the species distribution model with time series remote sensing. Taking invasive saltcedar as an example, four methods (i.e., phenological time series remote sensing model, temporal partial unmixing method, multiyear spectral angle clustering model, and time series remote sensing-based spatially explicit species distribution model) were developed to achieve the objectives. Results indicated that the phenological time series remote sensing model could effectively map saltcedar distributions through characterizing the seasonal phenological dynamics of plant species throughout the year. The proposed temporal partial unmixing method, compared to conventional unmixing methods, could more accurately estimate saltcedar abundance within a pixel by exploiting the adequate temporal signatures of saltcedar. The multiyear spectral angle clustering model could guide the selection of the most representative remotely sensed image for repetitive saltcedar mapping over space and time. Through incorporating spatial autocorrelation, the species distribution model developed in the study could identify the suitable habitats of saltcedar at a fine spatial scale and locate appropriate areas at high risk of saltcedar infestation. Among 10 environmental variables, the distance to the river and the phenological attributes summarized by the time series remote sensing were regarded as the most important. These methods developed in the study provide new perspectives on how the continuous time series can be leveraged under various conditions to investigate the plant invasion dynamics.

A methodology for in-situ and remote sensing of microphysical and radiative properties of contrails as they evolve into cirrus

NASA Astrophysics Data System (ADS)

Jones, H. M.; Haywood, J.; Marenco, F.; O'Sullivan, D.; Meyer, J.; Thorpe, R.; Gallagher, M. W.; Krämer, M.; Bower, K. N.; Rädel, G.; Rap, A.; Forster, P.; Coe, H.

2012-03-01

Contrails and especially their evolution into cirrus-like clouds are thought to have very important effects on local and global radiation budgets, though are generally not well represented in global climate models. Lack of contrail parameterisations is due to the limited availability of in situ contrail measurements which are difficult to obtain. Here we present a methodology for successful sampling and interpretation of contrail microphysical and radiative data using both in situ and remote sensing instrumentation on board the FAAM BAe146 UK research aircraft as part of the COntrails Spreading Into Cirrus (COSIC) study. Forecast models were utilised to determine flight regions suitable for contrail formation and sampling; regions that were both free of cloud but showed a high probability of occurrence of air mass being supersaturated with respect to ice. The FAAM research aircraft, fitted with cloud microphysics probes and remote sensing instruments, formed a distinctive spiral-shaped contrail in the predicted area by flying in an orbit over the same ground position as the wind advected the contrails to the east. Parts of these contrails were sampled during the completion of four orbits, with sampled contrail regions being between 7 and 30 min old. Lidar measurements were useful for in-flight determination of the location and spatial extent of the contrails, and also to report extinction values that agreed well with those calculated from the microphysical data. A shortwave spectrometer was also able to detect the contrails, though the signal was weak due to the dispersion and evaporation of the contrails. Post-flight the UK Met Office NAME III dispersion model was successfully used as a tool for modelling the dispersion of the persistent contrail; determining its location and age, and determining when there was interference from other measured other aircraft contrails or when cirrus encroached on the area later in the flight. The persistent contrails were found to consist of small (~10 μm) plate-like crystals where growth of ice crystals to larger sizes (~100 μm) was detected when higher water vapour levels were present. Using the cloud microphysics data, extinction co-efficient values were calculated and found to be 0.01-1 km-1. The contrails formed during the flight (referred to as B587) were found to have a visible lifetime of ~40 min, and limited water vapour supply was thought to have suppressed ice crystal growth.

A methodology for in-situ and remote sensing of microphysical and radiative properties of contrails as they evolve into cirrus

NASA Astrophysics Data System (ADS)

Jones, H. M.; Haywood, J.; Marenco, F.; O'Sullivan, D.; Meyer, J.; Thorpe, R.; Gallagher, M. W.; Krämer, M.; Bower, K. N.; Rädel, G.; Rap, A.; Woolley, A.; Forster, P.; Coe, H.

2012-09-01

Contrails and especially their evolution into cirrus-like clouds are thought to have very important effects on local and global radiation budgets, though are generally not well represented in global climate models. Lack of contrail parameterisations is due to the limited availability of in situ contrail measurements which are difficult to obtain. Here we present a methodology for successful sampling and interpretation of contrail microphysical and radiative data using both in situ and remote sensing instrumentation on board the FAAM BAe146 UK research aircraft as part of the COntrails Spreading Into Cirrus (COSIC) study. Forecast models were utilised to determine flight regions suitable for contrail formation and sampling; regions that were both free of cloud but showed a high probability of occurrence of air mass being supersaturated with respect to ice. The FAAM research aircraft, fitted with cloud microphysics probes and remote sensing instruments, formed a distinctive spiral-shaped contrail in the predicted area by flying in an orbit over the same ground position as the wind advected the contrails to the east. Parts of these contrails were sampled during the completion of four orbits, with sampled contrail regions being between 7 and 30 min old. Lidar measurements were useful for in-flight determination of the location and spatial extent of the contrails, and also to report extinction values that agreed well with those calculated from the microphysical data. A shortwave spectrometer was also able to detect the contrails, though the signal was weak due to the dispersion and evaporation of the contrails. Post-flight the UK Met Office NAME III dispersion model was successfully used as a tool for modelling the dispersion of the persistent contrail; determining its location and age, and determining when there was interference from other measured aircraft contrails or when cirrus encroached on the area later in the flight. The persistent contrails were found to consist of small (~10 μm) plate-like crystals where growth of ice crystals to larger sizes (~100 μm) was typically detected when higher water vapour levels were present. Using the cloud microphysics data, extinction co-efficient values were calculated and found to be 0.01-1 km-1. The contrails formed during the flight (referred to as B587) were found to have a visible lifetime of ~40 min, and limited water vapour supply was thought to have suppressed ice crystal growth.

Effective surveillance strategies following a potential classical Swine Fever incursion in a remote wild pig population in North-Western Australia.

PubMed

Leslie, E; Cowled, B; Graeme Garner, M; Toribio, J-A L M L; Ward, M P

2014-10-01

Early disease detection and efficient methods of proving disease freedom can substantially improve the response to incursions of important transboundary animal diseases in previously free regions. We used a spatially explicit, stochastic disease spread model to simulate the spread of classical swine fever in wild pigs in a remote region of northern Australia and to assess the performance of disease surveillance strategies to detect infection at different time points and to delineate the size of the resulting outbreak. Although disease would likely be detected, simple random sampling was suboptimal. Radial and leapfrog sampling improved the effectiveness of surveillance at various stages of the simulated disease incursion. This work indicates that at earlier stages, radial sampling can reduce epidemic length and achieve faster outbreak delineation and control, but at later stages leapfrog sampling will outperform radial sampling in relation to supporting faster disease control with a less-extensive outbreak area. Due to the complexity of wildlife population dynamics and group behaviour, a targeted approach to surveillance needs to be implemented for the efficient use of resources and time. Using a more situation-based surveillance approach and accounting for disease distribution and the time period over which an epidemic has occurred is the best way to approach the selection of an appropriate surveillance strategy. © 2013 Blackwell Verlag GmbH.

Comparison of water vapor from observations and models in the Asian Monsoon UTLS region

NASA Astrophysics Data System (ADS)

Singer, C. E.; Clouser, B.; Gaeta, D. C.; Moyer, E. J.

2017-12-01

As part of the StratoClim campaign in July/August 2017, the Chicago Water Isotope Spectrometer (ChiWIS) made water vapor measurements from the mid-troposphere through the lower stratosphere (to 21 km altitude). We compare in-situ measurements with remote sensing observations and model projections both to validate measurements and to evalute the added value of high-precision in-situ sampling. Preliminary results and comparison with other StratoClim tracer measurements suggest that the UTLS region is highly structured, beyond what models or satellite instruments can capture, and that ChiWIS accurately captures these variations.

Application of Multi-Source Remote Sensing Image in Yunnan Province Grassland Resources Investigation

NASA Astrophysics Data System (ADS)

Li, J.; Wen, G.; Li, D.

2018-04-01

Trough mastering background information of Yunnan province grassland resources utilization and ecological conditions to improves grassland elaborating management capacity, it carried out grassland resource investigation work by Yunnan province agriculture department in 2017. The traditional grassland resource investigation method is ground based investigation, which is time-consuming and inefficient, especially not suitable for large scale and hard-to-reach areas. While remote sensing is low cost, wide range and efficient, which can reflect grassland resources present situation objectively. It has become indispensable grassland monitoring technology and data sources and it has got more and more recognition and application in grassland resources monitoring research. This paper researches application of multi-source remote sensing image in Yunnan province grassland resources investigation. First of all, it extracts grassland resources thematic information and conducts field investigation through BJ-2 high space resolution image segmentation. Secondly, it classifies grassland types and evaluates grassland degradation degree through high resolution characteristics of Landsat 8 image. Thirdly, it obtained grass yield model and quality classification through high resolution and wide scanning width characteristics of MODIS images and sample investigate data. Finally, it performs grassland field qualitative analysis through UAV remote sensing image. According to project area implementation, it proves that multi-source remote sensing data can be applied to the grassland resources investigation in Yunnan province and it is indispensable method.

A Ground Truthing Method for AVIRIS Overflights Using Canopy Absorption Spectra

NASA Technical Reports Server (NTRS)

Gamon, John A.; Serrano, Lydia; Roberts, Dar A.; Ustin, Susan L.

1996-01-01

Remote sensing for ecological field studies requires ground truthing for accurate interpretation of remote imagery. However, traditional vegetation sampling methods are time consuming and hard to relate to the scale of an AVIRIS scene. The large errors associated with manual field sampling, the contrasting formats of remote and ground data, and problems with coregistration of field sites with AVIRIS pixels can lead to difficulties in interpreting AVIRIS data. As part of a larger study of fire risk in the Santa Monica Mountains of southern California, we explored a ground-based optical method of sampling vegetation using spectrometers mounted both above and below vegetation canopies. The goal was to use optical methods to provide a rapid, consistent, and objective means of "ground truthing" that could be related both to AVIRIS imagery and to conventional ground sampling (e.g., plot harvests and pigment assays).

On the retrieval of sea ice thickness and snow depth using concurrent laser altimetry and L-band remote sensing data

NASA Astrophysics Data System (ADS)

Zhou, Lu; Xu, Shiming; Liu, Jiping; Wang, Bin

2018-03-01

The accurate knowledge of sea ice parameters, including sea ice thickness and snow depth over the sea ice cover, is key to both climate studies and data assimilation in operational forecasts. Large-scale active and passive remote sensing is the basis for the estimation of these parameters. In traditional altimetry or the retrieval of snow depth with passive microwave remote sensing, although the sea ice thickness and the snow depth are closely related, the retrieval of one parameter is usually carried out under assumptions over the other. For example, climatological snow depth data or as derived from reanalyses contain large or unconstrained uncertainty, which result in large uncertainty in the derived sea ice thickness and volume. In this study, we explore the potential of combined retrieval of both sea ice thickness and snow depth using the concurrent active altimetry and passive microwave remote sensing of the sea ice cover. Specifically, laser altimetry and L-band passive remote sensing data are combined using two forward models: the L-band radiation model and the isostatic relationship based on buoyancy model. Since the laser altimetry usually features much higher spatial resolution than L-band data from the Soil Moisture Ocean Salinity (SMOS) satellite, there is potentially covariability between the observed snow freeboard by altimetry and the retrieval target of snow depth on the spatial scale of altimetry samples. Statistically significant correlation is discovered based on high-resolution observations from Operation IceBridge (OIB), and with a nonlinear fitting the covariability is incorporated in the retrieval algorithm. By using fitting parameters derived from large-scale surveys, the retrievability is greatly improved compared with the retrieval that assumes flat snow cover (i.e., no covariability). Verifications with OIB data show good match between the observed and the retrieved parameters, including both sea ice thickness and snow depth. With detailed analysis, we show that the error of the retrieval mainly arises from the difference between the modeled and the observed (SMOS) L-band brightness temperature (TB). The narrow swath and the limited coverage of the sea ice cover by altimetry is the potential source of error associated with the modeling of L-band TB and retrieval. The proposed retrieval methodology can be applied to the basin-scale retrieval of sea ice thickness and snow depth, using concurrent passive remote sensing and active laser altimetry based on satellites such as ICESat-2 and WCOM.

Evaluation of the reactive nitrogen budget of the remote atmosphere in global models using airborne measurements

NASA Astrophysics Data System (ADS)

Murray, L. T.; Strode, S. A.; Fiore, A. M.; Lamarque, J. F.; Prather, M. J.; Thompson, C. R.; Peischl, J.; Ryerson, T. B.; Allen, H.; Blake, D. R.; Crounse, J. D.; Brune, W. H.; Elkins, J. W.; Hall, S. R.; Hintsa, E. J.; Huey, L. G.; Kim, M. J.; Moore, F. L.; Ullmann, K.; Wennberg, P. O.; Wofsy, S. C.

2017-12-01

Nitrogen oxides (NOx ≡ NO + NO2) in the background atmosphere are critical precursors for the formation of tropospheric ozone and OH, thereby exerting strong influence on surface air quality, reactive greenhouse gases, and ecosystem health. The impact of NOx on atmospheric composition and climate is sensitive to the relative partitioning of reactive nitrogen between NOx and longer-lived reservoir species of the total reactive nitrogen family (NOy) such as HNO3, HNO4, PAN and organic nitrates (RONO2). Unfortunately, global chemistry-climate models (CCMs) and chemistry-transport models (CTMs) have historically disagreed in their reactive nitrogen budgets outside of polluted continental regions, and we have lacked in situ observations with which to evaluate them. Here, we compare and evaluate the NOy budget of six global models (GEOS-Chem CTM, GFDL AM3 CCM, GISS E2.1 CCM, GMI CTM, NCAR CAM CCM, and UCI CTM) using new observations of total reactive nitrogen and its member species from the NASA Atmospheric Tomography (ATom) mission. ATom has now completed two of its four planned deployments sampling the remote Pacific and Atlantic basins of both hemispheres with a comprehensive suite of measurements for constraining reactive photochemistry. All six models have simulated conditions climatologically similar to the deployments. The GMI and GEOS-Chem CTMs have in addition performed hindcast simulations using the MERRA-2 reanalysis, and have been sampled along the flight tracks. We evaluate the performance of the models relative to the observations, and identify factors contributing to their disparate behavior using known differences in model oxidation mechanisms, heterogeneous loss pathways, lightning and surface emissions, and physical loss processes.

Development of mathematical techniques for the assimilation of remote sensing data into atmospheric models

NASA Technical Reports Server (NTRS)

Seinfeld, J. H. (Principal Investigator)

1982-01-01

The problem of the assimilation of remote sensing data into mathematical models of atmospheric pollutant species was investigated. The data assimilation problem is posed in terms of the matching of spatially integrated species burden measurements to the predicted three-dimensional concentration fields from atmospheric diffusion models. General conditions were derived for the reconstructability of atmospheric concentration distributions from data typical of remote sensing applications, and a computational algorithm (filter) for the processing of remote sensing data was developed.

Development of mathematical techniques for the assimilation of remote sensing data into atmospheric models

NASA Technical Reports Server (NTRS)

Seinfeld, J. H. (Principal Investigator)

1982-01-01

The problem of the assimilation of remote sensing data into mathematical models of atmospheric pollutant species was investigated. The problem is posed in terms of the matching of spatially integrated species burden measurements to the predicted three dimensional concentration fields from atmospheric diffusion models. General conditions are derived for the "reconstructability' of atmospheric concentration distributions from data typical of remote sensing applications, and a computational algorithm (filter) for the processing of remote sensing data is developed.

Final Report: Laser-Based Optical Trap for Remote Sampling of Interplanetary and Atmospheric Particulate Matter

NASA Technical Reports Server (NTRS)

Stysley, Paul

2016-01-01

Applicability to Early Stage Innovation NIAC Cutting edge and innovative technologies are needed to achieve the demanding requirements for NASA origin missions that require sample collection as laid out in the NRC Decadal Survey. This proposal focused on fully understanding the state of remote laser optical trapping techniques for capturing particles and returning them to a target site. In future missions, a laser-based optical trapping system could be deployed on a lander that would then target particles in the lower atmosphere and deliver them to the main instrument for analysis, providing remote access to otherwise inaccessible samples. Alternatively, for a planetary mission the laser could combine ablation and trapping capabilities on targets typically too far away or too hard for traditional drilling sampling systems. For an interstellar mission, a remote laser system could gather particles continuously at a safe distance; this would avoid the necessity of having a spacecraft fly through a target cloud such as a comet tail. If properly designed and implemented, a laser-based optical trapping system could fundamentally change the way scientists designand implement NASA missions that require mass spectroscopy and particle collection.

Salinity modeling by remote sensing in central and southern Iraq

NASA Astrophysics Data System (ADS)

Wu, W.; Mhaimeed, A. S.; Platonov, A.; Al-Shafie, W. M.; Abbas, A. M.; Al-Musawi, H. H.; Khalaf, A.; Salim, K. A.; Chrsiten, E.; De Pauw, E.; Ziadat, F.

2012-12-01

Salinization, leading to a significant loss of cultivated land and crop production, is one of the most active land degradation phenomena in the Mesopotamian region in Iraq. The objectives of this study (under the auspices of ACIAR and Italian Government) are to investigate the possibility to use remote sensing technology to establish salinity-sensitive models which can be further applied to local and regional salinity mapping and assessment. Case studies were conducted in three pilot sites namely Musaib, Dujaila and West Garraf in the central and southern Iraq. Fourteen spring (February - April), seven June and four summer Landsat ETM+ images in the period 2009-2012, RapidEye data (April 2012), and 95 field EM38 measurements undertaken in this spring and summer, 16 relevant soil laboratory analysis result (Dujaila) were employed in this study. The procedure we followed includes: (1) Atmospheric correction using FLAASH model; (2) Multispectral transformation of a set of vegetation and non-vegetation indices such as GDVI (Generalized Difference Vegetation Index), NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), SAVI (Soil Adjusted Vegetation Index), SARVI (Soil Adjusted and Atmospherically Resistant Vegetation Index), NDII (Normalized Difference Infrared Index), Principal Components and surface temperature (T); (3) Derivation of the spring maximum (Musaib) and annual maximum (Dujaila and West Garraf) value in each pixel of each index of the observed period to avoid problems related to crop rotation (e.g. fallow) and the SLC-Off gaps in ETM+ images; (4) Extraction of the values of each vegetation and non-vegetation index corresponding to the field sampling locations (about 3 to 5 controversial samples very close to the roads or located in fallow were excluded); and (5) Coupling remote sensing indices with the available EM38 and soil electrical conductivity (EC) data using multiple linear least-square regression model at the confidence level of 95% in a stepwise (forward) manner. The results reveal that soil salinity and EM38 readings are negatively correlated with the different vegetation indices, especially, GDVI and NDVI, and positively correlated with T. The models obtained for the pilot sites are presented in Table 1. Although we are still waiting for more laboratory analytical result and satellite imagery for more comprehensive analysis, it is clearly possible to build up salinity models by remote sensing, on which further salinity mapping and assessment can be based. It is also noted that among all the vegetation indices, GDVI is the best salinity indicator followed by NDVI and T. RapidEye image shows lower correlation with EM38 measurements and EC because fallow and crop rotation issue cannot be sorted out by one acquisition image.Table 1: Salinity models obtained from the pilot sitesNote: EMV- Vertical reading of EM38, EC - Electrical conductivity in dS/m

Remote sensing of life: polarimetric signatures of photosynthetic pigments as sensitive biomarkers

NASA Astrophysics Data System (ADS)

Berdyugina, Svetlana V.; Kuhn, Jeff R.; Harrington, David M.; Šantl-Temkiv, Tina; Messersmith, E. John

2016-01-01

We develop a polarimetry-based remote-sensing method for detecting and identifying life forms in distant worlds and distinguishing them from non-biological species. To achieve this we have designed and built a bio-polarimetric laboratory experiment BioPol for measuring optical polarized spectra of various biological and non-biological samples. Here we focus on biological pigments, which are common in plants and bacteria that employ them either for photosynthesis or for protection against reactive oxygen species. Photosynthesis, which provides organisms with the ability to use light as a source of energy, emerged early in the evolution of life on Earth. The ability to harvest such a significant energy resource could likely also develop on habited exoplanets. Thus, we investigate the detectability of biomolecules that can capture photons of particular wavelengths and contribute to storing their energy in chemical bonds. We have carried out laboratory spectropolarimetric measurements of a representative sample of plants containing various amounts of pigments such as chlorophyll, carotenoids and others. We have also measured a variety of non-biological samples (sands, rocks). Using our lab measurements, we have modelled intensity and polarized spectra of Earth-like planets having different surface coverage by photosynthetic organisms, deserted land and ocean, as well as clouds. Our results demonstrate that linearly polarized spectra provide very sensitive and rather unambiguous detection of photosynthetic pigments of various kinds. Our work paves the path towards analogous measurements of microorganisms and remote sensing of microbial ecology on the Earth and of extraterrestrial life on other planets and moons.

Model for equitable care and outcomes for remote full care hemodialysis units.

PubMed

Bernstein, Keevin; Zacharias, James; Blanchard, James F; Yu, B Nancy; Shaw, Souradet Y

2010-04-01

Remotely located patients not living close to a nephrologist present major challenges for providing care. Various models of remotely delivered care have been developed, with a gap in knowledge regarding the outcomes of these heterogeneous models. This report describes a satellite care model for remote full-care hemodialysis units managed homogenously in the province of Manitoba, Canada, without onsite nephrologists. Survival in remotely located full-care units is compared with a large, urban full-care center with onsite nephrologists. Data from a Canadian provincial dialysis registry were extracted on 2663 patients between 1990 and 2005. All-cause mortality after initiation of chronic hemodialysis was assessed with Cox proportional hazards regression. Both short-term (1 year) and long-term (2 to 5 years) survival were analyzed. Survival for patients receiving remotely delivered care was shown to be better than for those receiving care in the urban care center with this particular Canadian model of care. Furthermore, there was no difference when assessing short- and long-term survival. This was independent of distance from the urban center. Chronic hemodialysis patients receiving remotely delivered care in a specialized facility attain comparable, if not better survival outcomes than their urban counterparts with direct onsite nephrology care. This model can potentially be adapted to other underserviced areas, including increasingly larger urban centers.

A Low-Signal-to-Noise-Ratio Sensor Framework Incorporating Improved Nighttime Capabilities in DIRSIG

NASA Astrophysics Data System (ADS)

Rizzuto, Anthony P.

When designing new remote sensing systems, it is difficult to make apples-to-apples comparisons between designs because of the number of sensor parameters that can affect the final image. Using synthetic imagery and a computer sensor model allows for comparisons to be made between widely different sensor designs or between competing design parameters. Little work has been done in fully modeling low-SNR systems end-to-end for these types of comparisons. Currently DIRSIG has limited capability to accurately model nighttime scenes under new moon conditions or near large cities. An improved DIRSIG scene modeling capability is presented that incorporates all significant sources of nighttime radiance, including new models for urban glow and airglow, both taken from the astronomy community. A low-SNR sensor modeling tool is also presented that accounts for sensor components and noise sources to generate synthetic imagery from a DIRSIG scene. The various sensor parameters that affect SNR are discussed, and example imagery is shown with the new sensor modeling tool. New low-SNR detectors have recently been designed and marketed for remote sensing applications. A comparison of system parameters for a state-of-the-art low-SNR sensor is discussed, and a sample design trade study is presented for a hypothetical scene and sensor.

Microwave Remote Sensing Modeling of Ocean Surface Salinity and Winds Using an Empirical Sea Surface Spectrum

NASA Technical Reports Server (NTRS)

Yueh, Simon H.

2004-01-01

Active and passive microwave remote sensing techniques have been investigated for the remote sensing of ocean surface wind and salinity. We revised an ocean surface spectrum using the CMOD-5 geophysical model function (GMF) for the European Remote Sensing (ERS) C-band scatterometer and the Ku-band GMF for the NASA SeaWinds scatterometer. The predictions of microwave brightness temperatures from this model agree well with satellite, aircraft and tower-based microwave radiometer data. This suggests that the impact of surface roughness on microwave brightness temperatures and radar scattering coefficients of sea surfaces can be consistently characterized by a roughness spectrum, providing physical basis for using combined active and passive remote sensing techniques for ocean surface wind and salinity remote sensing.

Remote sensing inputs to landscape models which predict future spatial land use patterns for hydrologic models

NASA Technical Reports Server (NTRS)

Miller, L. D.; Tom, C.; Nualchawee, K.

1977-01-01

A tropical forest area of Northern Thailand provided a test case of the application of the approach in more natural surroundings. Remote sensing imagery subjected to proper computer analysis has been shown to be a very useful means of collecting spatial data for the science of hydrology. Remote sensing products provide direct input to hydrologic models and practical data bases for planning large and small-scale hydrologic developments. Combining the available remote sensing imagery together with available map information in the landscape model provides a basis for substantial improvements in these applications.

Observing and modeling dynamics in terrestrial gross primary productivity and phenology from remote sensing: An assessment using in-situ measurements

NASA Astrophysics Data System (ADS)

Verma, Manish K.

Terrestrial gross primary productivity (GPP) is the largest and most variable component of the carbon cycle and is strongly influenced by phenology. Realistic characterization of spatio-temporal variation in GPP and phenology is therefore crucial for understanding dynamics in the global carbon cycle. In the last two decades, remote sensing has become a widely-used tool for this purpose. However, no study has comprehensively examined how well remote sensing models capture spatiotemporal patterns in GPP, and validation of remote sensing-based phenology models is limited. Using in-situ data from 144 eddy covariance towers located in all major biomes, I assessed the ability of 10 remote sensing-based methods to capture spatio-temporal variation in GPP at annual and seasonal scales. The models are based on different hypotheses regarding ecophysiological controls on GPP and span a range of structural and computational complexity. The results lead to four main conclusions: (i) at annual time scale, models were more successful capturing spatial variability than temporal variability; (ii) at seasonal scale, models were more successful in capturing average seasonal variability than interannual variability; (iii) simpler models performed as well or better than complex models; and (iv) models that were best at explaining seasonal variability in GPP were different from those that were best able to explain variability in annual scale GPP. Seasonal phenology of vegetation follows bounded growth and decay, and is widely modeled using growth functions. However, the specific form of the growth function affects how phenological dynamics are represented in ecosystem and remote sensing-base models. To examine this, four different growth functions (the logistic, Gompertz, Mirror-Gompertz and Richards function) were assessed using remotely sensed and in-situ data collected at several deciduous forest sites. All of the growth functions provided good statistical representation of in-situ and remote sensing time series. However, the Richards function captured observed asymmetric dynamics that were not captured by the other functions. The timing of key phenophase transitions derived using the Richards function therefore agreed best with observations. This suggests that ecosystem models and remote-sensing algorithms would benefit from using the Richards function to represent phenological dynamics.

Remote control missile model test

NASA Technical Reports Server (NTRS)

Allen, Jerry M.; Shaw, David S.; Sawyer, Wallace C.

1989-01-01

An extremely large, systematic, axisymmetric body/tail fin data base was gathered through tests of an innovative missile model design which is described herein. These data were originally obtained for incorporation into a missile aerodynamics code based on engineering methods (Program MISSILE3), but can also be used as diagnostic test cases for developing computational methods because of the individual-fin data included in the data base. Detailed analysis of four sample cases from these data are presented to illustrate interesting individual-fin force and moment trends. These samples quantitatively show how bow shock, fin orientation, fin deflection, and body vortices can produce strong, unusual, and computationally challenging effects on individual fin loads. Comparisons between these data and calculations from the SWINT Euler code are also presented.

Utilizing remote sensing of Thematic Mapper data to improve our understanding of estuarine processes and their influence on the productivity of estuarine-dependent fisheries

NASA Technical Reports Server (NTRS)

Browder, J. A.; May, L. N., Jr.; Rosenthal, A.; Baumann, R. H.; Gosselink, J. G.

1986-01-01

LANDSAT thematic mapper (TM) data are being used to refine and validate a stochastic spatial computer model to be applied to coastal resource management problems in Louisiana. Two major aspects of the research are: (1) the measurement of area of land (or emergent vegetation) and water and the length of the interface between land and water in TM imagery of selected coastal wetlands (sample marshes); and (2) the comparison of spatial patterns of land and water in the sample marshes of the imagery to that in marshes simulated by a computer model. In addition to activities in these two areas, the potential use of a published autocorrelation statistic is analyzed.

Mobile inductively coupled plasma system

DOEpatents

D`Silva, A.P.; Jaselskis, E.J.

1999-03-30

A system is described for sampling and analyzing a material located at a hazardous site. A laser located remotely from the hazardous site is connected to an optical fiber, which directs laser radiation proximate the material at the hazardous site. The laser radiation abates a sample of the material. An inductively coupled plasma is located remotely from the material. An aerosol transport system carries the ablated particles to a plasma, where they are dissociated, atomized and excited to provide characteristic optical reduction of the elemental constituents of the sample. An optical spectrometer is located remotely from the site. A second optical fiber is connected to the optical spectrometer at one end and the plasma source at the other end to carry the optical radiation from the plasma source to the spectrometer. 10 figs.

Predictors of Antenatal Care, Skilled Birth Attendance, and Postnatal Care Utilization among the Remote and Poorest Rural Communities of Zambia: A Multilevel Analysis.

PubMed

Jacobs, Choolwe; Moshabela, Mosa; Maswenyeho, Sitali; Lambo, Nildah; Michelo, Charles

2017-01-01

Optimal utilization of maternal health-care services is associated with reduction of mortality and morbidity for both mothers and their neonates. However, deficiencies and disparity in the use of key maternal health services within most developing countries still persist. We examined patterns and predictors associated with the utilization of specific indicators for maternal health services among mothers living in the poorest and remote district populations of Zambia. A cross-sectional baseline household survey was conducted in May 2012. A total of 551 mothers with children between the ages 0 and 5 months were sampled from 29 catchment areas in four rural and remote districts of Zambia using the lot quality assurance sampling method. Using multilevel modeling, we accounted for individual- and community-level factors associated with utilization of maternal health-care services, with a focus on antenatal care (ANC), skilled birth attendance (SBA), and postnatal care (PNC). Utilization rates of focused ANC, SBA, and PNC within 48 h were 30, 37, and 28%, respectively. The mother's ability to take an HIV test and receiving test results and uptake of intermittent preventive treatment for malaria were positive predictors of focused ANC. Receiving ANC at least once from skilled personnel was a significant predictor of SBA and PNC within 48 h after delivery. Women who live in centralized rural areas were more likely to use SBA than those living in remote rural areas. Utilization of maternal health services by mothers living among the remote and poor marginalized populations of Zambia is much lower than the national averages. Finding that women that receive ANC once from a skilled attendant among the remote and poorest populations are more likely to have a SBA and PNC, suggests the importance of contact with a skilled health worker even if it is just once, in influencing use of services. Therefore, it appears that in order for women in these marginalized communities to benefit from SBA and PNC, it is important for them to have at least one ANC provided by a skilled personnel, rather than non-skilled health-care providers.

Assimilation of remote sensing observations into a sediment transport model of China's largest freshwater lake: spatial and temporal effects.

PubMed

Zhang, Peng; Chen, Xiaoling; Lu, Jianzhong; Zhang, Wei

2015-12-01

Numerical models are important tools that are used in studies of sediment dynamics in inland and coastal waters, and these models can now benefit from the use of integrated remote sensing observations. This study explores a scheme for assimilating remotely sensed suspended sediment (from charge-coupled device (CCD) images obtained from the Huanjing (HJ) satellite) into a two-dimensional sediment transport model of Poyang Lake, the largest freshwater lake in China. Optimal interpolation is used as the assimilation method, and model predictions are obtained by combining four remote sensing images. The parameters for optimal interpolation are determined through a series of assimilation experiments evaluating the sediment predictions based on field measurements. The model with assimilation of remotely sensed sediment reduces the root-mean-square error of the predicted sediment concentrations by 39.4% relative to the model without assimilation, demonstrating the effectiveness of the assimilation scheme. The spatial effect of assimilation is explored by comparing model predictions with remotely sensed sediment, revealing that the model with assimilation generates reasonable spatial distribution patterns of suspended sediment. The temporal effect of assimilation on the model's predictive capabilities varies spatially, with an average temporal effect of approximately 10.8 days. The current velocities which dominate the rate and direction of sediment transport most likely result in spatial differences in the temporal effect of assimilation on model predictions.

Multistage remote sensing: toward an annual national inventory

Treesearch

Raymond L. Czaplewski

1999-01-01

Remote sensing can improve efficiency of statistical information. Landsat data can identify and map a few broad categories of forest cover and land use. However, more-detailed information requires a sample of higher-resolution imagery, which costs less than field data but considerably more than Landsat data. A national remote sensing program would be a major...

Remote sensing-based predictors improve distribution models of rare, early successional and boradleaf tree species in Utah

Treesearch

N. E. Zimmermann; T. C. Edwards; G. G. Moisen; T. S. Frescino; J. A. Blackard

2007-01-01

Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species...

Offshore Wind Resource Characterization | Wind | NREL

Science.gov Websites

identify critical data needed. Remote Sensing and Modeling Photo of the SeaZephIR Prototype at sea. 2009 techniques such as remote sensing and modeling to provide data on design conditions. Research includes comparing the data provided by remote sensing devices and models to data collected by traditional methods

Prevalence of Posttraumatic Stress Disorder in Remotely Piloted Aircraft Operators in the United States Air Force

DTIC Science & Technology

2016-05-24

common feature of the depressive and anxiety disorders: a test of the revised integrative hierarchical model in a national sample. J Abnorm Psychol...proliferation of this unique form of warfare, concerns have been raised regarding the psychological impact such operations have on RPA operators directly...and clinical interviews utilizing the Clinician Administered Psychological Survey to determine the nature of the respondents’ stressful military

Airborne Remote sensing of the OH tropospheric column with an Integrated Path Differential LIDAR.

NASA Astrophysics Data System (ADS)

Hanisco, T. F.; Liang, Q.; Nicely, J. M.; Brune, W. H.; Miller, D. O.; Thames, A. B.

2017-12-01

The Hydroxyl radical, OH, is central to the photochemistry that controls tropospheric oxidation including the removal of atmospheric methane. Measurements of this important species are thus critical to testing our understanding and for constraining model results. Until now, tropospheric measurements have been limited to airborne or ground-based in situ instruments best suited to test photochemical box models. However, because of the growing recognition of the importance of the global methane abundance, we have a growing need to better quantify OH at the regional to global scales that are best sampled with airborne or space-based remote sensing instruments. To address this need, we have developed an instrument concept and have begun work on a laser transmitter for an airborne integrated path differential absorption LIDAR for the detection of OH. We will describe the instrument and present the expected performance characteristics. As a demonstration, we will use measurements from the recent ATOM-1 NASA airborne campaign to show measured OH columns can be used to constrain regional and global models.

Modelling soil salinity in Oued El Abid watershed, Morocco

NASA Astrophysics Data System (ADS)

Mouatassime Sabri, El; Boukdir, Ahmed; Karaoui, Ismail; Arioua, Abdelkrim; Messlouhi, Rachid; El Amrani Idrissi, Abdelkhalek

2018-05-01

Soil salinisation is a phenomenon considered to be a real threat to natural resources in semi-arid climates. The phenomenon is controlled by soil (texture, depth, slope etc.), anthropogenic factors (drainage system, irrigation, crops types, etc.), and climate factors. This study was conducted in the watershed of Oued El Abid in the region of Beni Mellal-Khenifra, aimed at localising saline soil using remote sensing and a regression model. The spectral indices were extracted from Landsat imagery (30 m resolution). A linear correlation of electrical conductivity, which was calculated based on soil samples (ECs), and the values extracted based on spectral bands showed a high accuracy with an R2 (Root square) of 0.80. This study proposes a new spectral salinity index using Landsat bands B1 and B4. This hydro-chemical and statistical study, based on a yearlong survey, showed a moderate amount of salinity, which threatens dam water quality. The results present an improved ability to use remote sensing and regression model integration to detect soil salinity with high accuracy and low cost, and permit intervention at an early stage of salinisation.

Exploring the Benefits of Molecular Testing for Gonorrhoea Antibiotic Resistance Surveillance in Remote Settings.

PubMed

Hui, Ben B; Ryder, Nathan; Su, Jiunn-Yih; Ward, James; Chen, Marcus Y; Donovan, Basil; Fairley, Christopher K; Guy, Rebecca J; Lahra, Monica M; Law, Mathew G; Whiley, David M; Regan, David G

2015-01-01

Surveillance for gonorrhoea antimicrobial resistance (AMR) is compromised by a move away from culture-based testing in favour of more convenient nucleic acid amplification test (NAAT) tests. We assessed the potential benefit of a molecular resistance test in terms of the timeliness of detection of gonorrhoea AMR. An individual-based mathematical model was developed to describe the transmission of gonorrhoea in a remote Indigenous population in Australia. We estimated the impact of the molecular test on the time delay between first importation and the first confirmation that the prevalence of gonorrhoea AMR (resistance proportion) has breached the WHO-recommended 5% threshold (when a change in antibiotic should occur). In the remote setting evaluated in this study, the model predicts that when culture is the only available means of testing for AMR, the breach will only be detected when the actual prevalence of AMR in the population has already reached 8 - 18%, with an associated delay of ~43 - 69 months between first importation and detection. With the addition of a molecular resistance test, the number of samples for which AMR can be determined increases facilitating earlier detection at a lower resistance proportion. For the best case scenario, where AMR can be determined for all diagnostic samples, the alert would be triggered at least 8 months earlier than using culture alone and the resistance proportion will have only slightly exceeded the 5% notification threshold. Molecular tests have the potential to provide more timely warning of the emergence of gonorrhoea AMR. This in turn will facilitate earlier treatment switching and more targeted treatment, which has the potential to reduce the population impact of gonorrhoea AMR.

Exploring the Benefits of Molecular Testing for Gonorrhoea Antibiotic Resistance Surveillance in Remote Settings

PubMed Central

Hui, Ben B.; Ryder, Nathan; Su, Jiunn-Yih; Ward, James; Chen, Marcus Y.; Donovan, Basil; Fairley, Christopher K.; Guy, Rebecca J.; Lahra, Monica M.; Law, Mathew G.; Whiley, David M.; Regan, David G.

2015-01-01

Background Surveillance for gonorrhoea antimicrobial resistance (AMR) is compromised by a move away from culture-based testing in favour of more convenient nucleic acid amplification test (NAAT) tests. We assessed the potential benefit of a molecular resistance test in terms of the timeliness of detection of gonorrhoea AMR. Methods and Findings An individual-based mathematical model was developed to describe the transmission of gonorrhoea in a remote Indigenous population in Australia. We estimated the impact of the molecular test on the time delay between first importation and the first confirmation that the prevalence of gonorrhoea AMR (resistance proportion) has breached the WHO-recommended 5% threshold (when a change in antibiotic should occur). In the remote setting evaluated in this study, the model predicts that when culture is the only available means of testing for AMR, the breach will only be detected when the actual prevalence of AMR in the population has already reached 8 – 18%, with an associated delay of ~43 – 69 months between first importation and detection. With the addition of a molecular resistance test, the number of samples for which AMR can be determined increases facilitating earlier detection at a lower resistance proportion. For the best case scenario, where AMR can be determined for all diagnostic samples, the alert would be triggered at least 8 months earlier than using culture alone and the resistance proportion will have only slightly exceeded the 5% notification threshold. Conclusions Molecular tests have the potential to provide more timely warning of the emergence of gonorrhoea AMR. This in turn will facilitate earlier treatment switching and more targeted treatment, which has the potential to reduce the population impact of gonorrhoea AMR. PMID:26181042

Remote Handled WIPP Canisters at Los Alamos National Laboratory Characterized for Retrieval

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

Griffin, J.; Gonzales, W.

2007-07-01

The Los Alamos National Laboratory (LANL) is pursuing retrieval, transportation, and disposal of 16 remote handled transuranic waste canisters stored below ground in shafts since 1994. These canisters were retrievably stored in the shafts to await Nuclear Regulatory Commission certification of the Model Number RH-TRU 72B transportation cask and authorization of the Waste Isolation Pilot Plant (WIPP) to accept the canisters for disposal. Retrieval planning included radiological characterization and visual inspection of the canisters to confirm historical records, verify container integrity, determine proper personnel protection for the retrieval operations, provide radiological dose and exposure rate data for retrieval operations, andmore » to provide exterior radiological contamination data. The radiological characterization and visual inspection of the canisters was performed in May 2006. The effort required the development of remote techniques and equipment due to the potential for personnel exposure to radiological doses approaching 300 R/hr. Innovations included the use of two nested 1.5 meter (m) (5-feet [ft]) long concrete culvert pipes (1.1-m [42 inch (in.)] and 1.5-m [60-in] diameter, respectively) as radiological shielding and collapsible electrostatic dusting wands to collect radiological swipe samples from the annular space between the canister and shaft wall. Visual inspection indicated that the canisters are in good condition with little or no rust, the welded seams are intact, and ten of the canisters include hydrogen gas sampling equipment on the pintle that will have to be removed prior to retrieval. The visual inspection also provided six canister identification numbers that matched historical storage records. The exterior radiological data indicated alpha and beta contamination below LANL release criteria and radiological dose and exposure rates lower than expected based upon historical data and modeling of the canister contents. (authors)« less

Linear models for airborne-laser-scanning-based operational forest inventory with small field sample size and highly correlated LiDAR data

USGS Publications Warehouse

Junttila, Virpi; Kauranne, Tuomo; Finley, Andrew O.; Bradford, John B.

2015-01-01

Modern operational forest inventory often uses remotely sensed data that cover the whole inventory area to produce spatially explicit estimates of forest properties through statistical models. The data obtained by airborne light detection and ranging (LiDAR) correlate well with many forest inventory variables, such as the tree height, the timber volume, and the biomass. To construct an accurate model over thousands of hectares, LiDAR data must be supplemented with several hundred field sample measurements of forest inventory variables. This can be costly and time consuming. Different LiDAR-data-based and spatial-data-based sampling designs can reduce the number of field sample plots needed. However, problems arising from the features of the LiDAR data, such as a large number of predictors compared with the sample size (overfitting) or a strong correlation among predictors (multicollinearity), may decrease the accuracy and precision of the estimates and predictions. To overcome these problems, a Bayesian linear model with the singular value decomposition of predictors, combined with regularization, is proposed. The model performance in predicting different forest inventory variables is verified in ten inventory areas from two continents, where the number of field sample plots is reduced using different sampling designs. The results show that, with an appropriate field plot selection strategy and the proposed linear model, the total relative error of the predicted forest inventory variables is only 5%–15% larger using 50 field sample plots than the error of a linear model estimated with several hundred field sample plots when we sum up the error due to both the model noise variance and the model’s lack of fit.

Feasibility of remote sensing for detecting thermal pollution. Part 1: Feasibility study. Part 2: Implementation plan. [coastal ecology

NASA Technical Reports Server (NTRS)

Veziroglu, T. N.; Lee, S. S.

1973-01-01

A feasibility study for the development of a three-dimensional generalized, predictive, analytical model involving remote sensing, in-situ measurements, and an active system to remotely measure turbidity is presented. An implementation plan for the development of the three-dimensional model and for the application of remote sensing of temperature and turbidity measurements is outlined.

Atmospheric transmission computer program CP

NASA Technical Reports Server (NTRS)

Pitts, D. E.; Barnett, T. L.; Korb, C. L.; Hanby, W.; Dillinger, A. E.

1974-01-01

A computer program is described which allows for calculation of the effects of carbon dioxide, water vapor, methane, ozone, carbon monoxide, and nitrous oxide on earth resources remote sensing techniques. A flow chart of the program and operating instructions are provided. Comparisons are made between the atmospheric transmission obtained from laboratory and spacecraft spectrometer data and that obtained from a computer prediction using a model atmosphere and radiosonde data. Limitations of the model atmosphere are discussed. The computer program listings, input card formats, and sample runs for both radiosonde data and laboratory data are included.

Sample push-out fixture

DOEpatents

Biernat, John L.

2002-11-05

This invention generally relates to the remote removal of pelletized samples from cylindrical containment capsules. V-blocks are used to receive the samples and provide guidance to push out rods. Stainless steel liners fit into the v-channels on the v-blocks which permits them to be remotely removed and replaced or cleaned to prevent cross contamination between capsules and samples. A capsule holder securely holds the capsule while allowing manual up/down and in/out movement to align each sample hole with the v-blocks. Both end sections contain identical v-blocks; one that guides the drive out screw and rods or manual push out rods and the other to receive the samples as they are driven out of the capsule.

Seasonal changes in the tropospheric carbon monoxide profile over the remote Southern Hemisphere evaluated using multi-model simulations and aircraft observations

NASA Astrophysics Data System (ADS)

Fisher, J. A.; Wilson, S. R.; Zeng, G.; Williams, J. E.; Emmons, L. K.; Langenfelds, R. L.; Krummel, P. B.; Steele, L. P.

2015-03-01

The combination of low anthropogenic emissions and large biogenic sources that characterizes the Southern Hemisphere (SH) leads to significant differences in atmospheric composition relative to the better studied Northern Hemisphere. This unique balance of sources poses significant challenges for global models. Carbon monoxide (CO) in particular is difficult to simulate in the SH due to the increased importance of secondary chemical production associated with the much more limited primary emissions. Here, we use aircraft observations from the 1991-2000 Cape Grim Overflight Program (CGOP) and the 2009-2011 HIAPER (High-performance Instrumented Airborne Platform for Environmental Research) Pole-to-Pole Observations (HIPPO), together with model output from the SH Model Intercomparison Project, to elucidate the drivers of CO vertical structure in the remote SH. Observed CO vertical profiles from Cape Grim are remarkably consistent with those observed over the southern mid-latitudes Pacific 10-20 years later, despite major differences in time periods, flight locations, and sampling strategies between the two data sets. These similarities suggest the processes driving observed vertical gradients are coherent across much of the remote SH and have not changed significantly over the past 2 decades. Model ability to simulate CO profiles reflects the interplay between biogenic emission sources, the chemical mechanisms that drive CO production from these sources, and the transport that redistributes this CO throughout the SH. The four chemistry-climate and chemical transport models included in the intercomparison show large variability in their abilities to reproduce the observed CO profiles. In particular, two of the four models significantly underestimate vertical gradients in austral summer and autumn, which we find are driven by long-range transport of CO produced from oxidation of biogenic compounds. Comparisons between the models show that more complex chemical mechanisms do not necessarily provide more accurate simulation of CO vertical gradients due to the convolved impacts of emissions, chemistry, and transport. Our results imply a large sensitivity of the remote SH troposphere to biogenic emissions and chemistry, both of which remain key uncertainties in global modeling. We suggest that the CO vertical gradient can be used as a metric for future model evaluation as it provides a sensitive test of the processes that define the chemical state of the background atmosphere.

Modelisation de l'architecture des forets pour ameliorer la teledetection des attributs forestiers

NASA Astrophysics Data System (ADS)

Cote, Jean-Francois

The quality of indirect measurements of canopy structure, from in situ and satellite remote sensing, is based on knowledge of vegetation canopy architecture. Technological advances in ground-based, airborne or satellite remote sensing can now significantly improve the effectiveness of measurement programs on forest resources. The structure of vegetation canopy describes the position, orientation, size and shape of elements of the canopy. The complexity of the canopy in forest environments greatly limits our ability to characterize forest structural attributes. Architectural models have been developed to help the interpretation of canopy structural measurements by remote sensing. Recently, the terrestrial LiDAR systems, or TLiDAR (Terrestrial Light Detection and Ranging), are used to gather information on the structure of individual trees or forest stands. The TLiDAR allows the extraction of 3D structural information under the canopy at the centimetre scale. The methodology proposed in my Ph.D. thesis is a strategy to overcome the weakness in the structural sampling of vegetation cover. The main objective of the Ph.D. is to develop an architectural model of vegetation canopy, called L-Architect (LiDAR data to vegetation Architecture), and to focus on the ability to document forest sites and to get information on canopy structure from remote sensing tools. Specifically, L-Architect reconstructs the architecture of individual conifer trees from TLiDAR data. Quantitative evaluation of L-Architect consisted to investigate (i) the structural consistency of the reconstructed trees and (ii) the radiative coherence by the inclusion of reconstructed trees in a 3D radiative transfer model. Then, a methodology was developed to quasi-automatically reconstruct the structure of individual trees from an optimization algorithm using TLiDAR data and allometric relationships. L-Architect thus provides an explicit link between the range measurements of TLiDAR and structural attributes of individual trees. L-Architect has finally been applied to model the architecture of forest canopy for better characterization of vertical and horizontal structure with airborne LiDAR data. This project provides a mean to answer requests of detailed canopy architectural data, difficult to obtain, to reproduce a variety of forest covers. Because of the importance of architectural models, L-Architect provides a significant contribution for improving the capacity of parameters' inversion in vegetation cover for optical and lidar remote sensing. Mots-cles: modelisation architecturale, lidar terrestre, couvert forestier, parametres structuraux, teledetection.

Development of a remote digital augmentation system and application to a remotely piloted research vehicle

NASA Technical Reports Server (NTRS)

Edwards, J. W.; Deets, D. A.

1975-01-01

A cost-effective approach to flight testing advanced control concepts with remotely piloted vehicles is described. The approach utilizes a ground based digital computer coupled to the remotely piloted vehicle's motion sensors and control surface actuators through telemetry links to provide high bandwidth feedback control. The system was applied to the control of an unmanned 3/8-scale model of the F-15 airplane. The model was remotely augmented; that is, the F-15 mechanical and control augmentation flight control systems were simulated by the ground-based computer, rather than being in the vehicle itself. The results of flight tests of the model at high angles of attack are discussed.

The feasibility of using a universal Random Forest model to map tree height across different locations and vegetation types

NASA Astrophysics Data System (ADS)

Su, Y.; Guo, Q.; Jin, S.; Gao, S.; Hu, T.; Liu, J.; Xue, B. L.

2017-12-01

Tree height is an important forest structure parameter for understanding forest ecosystem and improving the accuracy of global carbon stock quantification. Light detection and ranging (LiDAR) can provide accurate tree height measurements, but its use in large-scale tree height mapping is limited by the spatial availability. Random Forest (RF) has been one of the most commonly used algorithms for mapping large-scale tree height through the fusion of LiDAR and other remotely sensed datasets. However, how the variances in vegetation types, geolocations and spatial scales of different study sites influence the RF results is still a question that needs to be addressed. In this study, we selected 16 study sites across four vegetation types in United States (U.S.) fully covered by airborne LiDAR data, and the area of each site was 100 km2. The LiDAR-derived canopy height models (CHMs) were used as the ground truth to train the RF algorithm to predict canopy height from other remotely sensed variables, such as Landsat TM imagery, terrain information and climate surfaces. To address the abovementioned question, 22 models were run under different combinations of vegetation types, geolocations and spatial scales. The results show that the RF model trained at one specific location or vegetation type cannot be used to predict tree height in other locations or vegetation types. However, by training the RF model using samples from all locations and vegetation types, a universal model can be achieved for predicting canopy height across different locations and vegetation types. Moreover, the number of training samples and the targeted spatial resolution of the canopy height product have noticeable influence on the RF prediction accuracy.

Quantifying Uncertainties from Presence Data Sampling Methods for Species Distribution Modeling: Focused on Vegetation.

NASA Astrophysics Data System (ADS)

Sung, S.; Kim, H. G.; Lee, D. K.; Park, J. H.; Mo, Y.; Kil, S.; Park, C.

2016-12-01

The impact of climate change has been observed throughout the globe. The ecosystem experiences rapid changes such as vegetation shift, species extinction. In these context, Species Distribution Model (SDM) is one of the popular method to project impact of climate change on the ecosystem. SDM basically based on the niche of certain species with means to run SDM present point data is essential to find biological niche of species. To run SDM for plants, there are certain considerations on the characteristics of vegetation. Normally, to make vegetation data in large area, remote sensing techniques are used. In other words, the exact point of presence data has high uncertainties as we select presence data set from polygons and raster dataset. Thus, sampling methods for modeling vegetation presence data should be carefully selected. In this study, we used three different sampling methods for selection of presence data of vegetation: Random sampling, Stratified sampling and Site index based sampling. We used one of the R package BIOMOD2 to access uncertainty from modeling. At the same time, we included BioCLIM variables and other environmental variables as input data. As a result of this study, despite of differences among the 10 SDMs, the sampling methods showed differences in ROC values, random sampling methods showed the lowest ROC value while site index based sampling methods showed the highest ROC value. As a result of this study the uncertainties from presence data sampling methods and SDM can be quantified.

a Cloud Boundary Detection Scheme Combined with Aslic and Cnn Using ZY-3, GF-1/2 Satellite Imagery

NASA Astrophysics Data System (ADS)

Guo, Z.; Li, C.; Wang, Z.; Kwok, E.; Wei, X.

2018-04-01

Remote sensing optical image cloud detection is one of the most important problems in remote sensing data processing. Aiming at the information loss caused by cloud cover, a cloud detection method based on convolution neural network (CNN) is presented in this paper. Firstly, a deep CNN network is used to extract the multi-level feature generation model of cloud from the training samples. Secondly, the adaptive simple linear iterative clustering (ASLIC) method is used to divide the detected images into superpixels. Finally, the probability of each superpixel belonging to the cloud region is predicted by the trained network model, thereby generating a cloud probability map. The typical region of GF-1/2 and ZY-3 were selected to carry out the cloud detection test, and compared with the traditional SLIC method. The experiment results show that the average accuracy of cloud detection is increased by more than 5 %, and it can detected thin-thick cloud and the whole cloud boundary well on different imaging platforms.

Fluid sampling apparatus and method

DOEpatents

Yeamans, David R.

1998-01-01

Incorporation of a bellows in a sampling syringe eliminates ingress of contaminants, permits replication of amounts and compression of multiple sample injections, and enables remote sampling for off-site analysis.

Remote sensing applications to hydrologic modeling

NASA Technical Reports Server (NTRS)

Dozier, J.; Estes, J. E.; Simonett, D. S.; Davis, R.; Frew, J.; Marks, D.; Schiffman, K.; Souza, M.; Witebsky, E.

1977-01-01

An energy balance snowmelt model for rugged terrain was devised and coupled to a flow model. A literature review of remote sensing applications to hydrologic modeling was included along with a software development outline.

The Tolman Surface Brightness Test for the Reality of the Expansion. V. Provenance of the Test and a New Representation of the Data for Three Remote Hubble Space Telescope Galaxy Clusters

NASA Astrophysics Data System (ADS)

Sandage, Allan

2010-02-01

A new reduction is made of the Hubble Space Telescope (HST) photometric data for E galaxies in three remote clusters at redshifts near z = 0.85 in search for the Tolman surface brightness (SB) signal for the reality of the expansion. Because of the strong variation of SB of such galaxies with intrinsic size, and because the Tolman test is about SB, we must account for the variation. In an earlier version of the test, Lubin & Sandage calibrated the variation out. In contrast, the test is made here using fixed radius bins for both the local and remote samples. Homologous positions in the galaxy image at which to compare the SB values are defined by radii at five Petrosian η values ranging from 1.0 to 2.0. Sérsic luminosity profiles are used to generate two diagnostic diagrams that define the mean SB distribution across the galaxy image. A Sérsic exponent, defined by the rn family of Sérsic profiles, of n = 0.46 fits both the local and remote samples, on average, with only a small spread from 0.4 to 0.6. Diagrams of the dimming of the langSBrang with redshift over the range of Petrosian η radii shows a highly significant Tolman signal but degraded by luminosity evolution in the look-back time. The expansion is real and a luminosity evolution exists at the mean redshift of the HST clusters of 0.8 mag in R cape and 0.4 mag in the I cape photometric rest-frame bands, consistent with the evolution models of Bruzual & Charlot.

Field test of available methods to measure remotely SO2 and NOx emissions from ships

NASA Astrophysics Data System (ADS)

Balzani Lööv, J. M.; Alfoldy, B.; Beecken, J.; Berg, N.; Berkhout, A. J. C.; Duyzer, J.; Gast, L. F. L.; Hjorth, J.; Jalkanen, J.-P.; Lagler, F.; Mellqvist, J.; Prata, F.; van der Hoff, G. R.; Westrate, H.; Swart, D. P. J.; Borowiak, A.

2013-11-01

Methods for the determination of ship fuel sulphur content and NOx emission factors from remote measurements have been compared in the harbour of Rotterdam and compared to direct stack emission measurements on the ferry Stena Hollandica. The methods were selected based on a review of the available literature on ship emission measurements. They were either optical (LIDAR, DOAS, UV camera), combined with model based estimates of fuel consumption, or based on the so called "sniffer" principle, where SO2 or NOx emission factors are determined from simultaneous measurement of the increase of CO2 and SO2 or NOx concentrations in the plume of the ship compared to the background. The measurements were performed from stations at land, from a boat, and from a helicopter. Mobile measurement platforms were found to have important advantages compared to the landbased ones because they allow to optimize the sampling conditions and to sample from ships on the open sea. Although optical methods can provide reliable results, it was found that at the state of the art, the "sniffer" approach is the most convenient technique for determining both SO2 and NOx emission factors remotely. The average random error on the determination of SO2 emission factors comparing two identical instrumental set-ups was 6%. However, it was found that apparently minor differences in the instrumental characteristics, such as response time, could cause significant differences between the emission factors determined. Direct stack measurements showed that about 14% of the fuel sulphur content was not emitted as SO2. This was supported by the remote measurements and is in agreement with the results of other field studies.

Field test of available methods to measure remotely SOx and NOx emissions from ships

NASA Astrophysics Data System (ADS)

Balzani Lööv, J. M.; Alfoldy, B.; Gast, L. F. L.; Hjorth, J.; Lagler, F.; Mellqvist, J.; Beecken, J.; Berg, N.; Duyzer, J.; Westrate, H.; Swart, D. P. J.; Berkhout, A. J. C.; Jalkanen, J.-P.; Prata, A. J.; van der Hoff, G. R.; Borowiak, A.

2014-08-01

Methods for the determination of ship fuel sulphur content and NOx emission factors based on remote measurements have been compared in the harbour of Rotterdam and compared to direct stack emission measurements on the ferry Stena Hollandica. The methods were selected based on a review of the available literature on ship emission measurements. They were either optical (LIDAR, Differential Optical Absorption Spectroscopy (DOAS), UV camera), combined with model-based estimates of fuel consumption, or based on the so called "sniffer" principle, where SO2 or NOx emission factors are determined from simultaneous measurement of the increase of CO2 and SO2 or NOx concentrations in the plume of the ship compared to the background. The measurements were performed from stations at land, from a boat and from a helicopter. Mobile measurement platforms were found to have important advantages compared to the land-based ones because they allow optimizing the sampling conditions and sampling from ships on the open sea. Although optical methods can provide reliable results it was found that at the state of the art level, the "sniffer" approach is the most convenient technique for determining both SO2 and NOx emission factors remotely. The average random error on the determination of SO2 emission factors comparing two identical instrumental set-ups was 6%. However, it was found that apparently minor differences in the instrumental characteristics, such as response time, could cause significant differences between the emission factors determined. Direct stack measurements showed that about 14% of the fuel sulphur content was not emitted as SO2. This was supported by the remote measurements and is in agreement with the results of other field studies.

Developing a thermal characteristic index for lithology identification using thermal infrared remote sensing data

NASA Astrophysics Data System (ADS)

Wei, Jiali; Liu, Xiangnan; Ding, Chao; Liu, Meiling; Jin, Ming; Li, Dongdong

2017-01-01

In remote sensing petrology fields, studies have mainly concentrated on spectroscopy remote sensing research, and methods to identify minerals and rocks are mainly based on the analysis and enhancement of spectral features. Few studies have reported the application of thermodynamics for lithology identification. This paper aims to establish a thermal characteristic index (TCI) to explore rock thermal behavior responding to defined environmental systems. The study area is located in the northern Qinghai Province, China, on the northern edge of the Qinghai-Tibet Plateau, where mafic-ultramafic rock, quartz-rich rock, alkali granite rock and carbonate rock are well exposed; the pixel samples of these rocks and vegetation were obtained based on relevant indices and geological maps. The scatter plots of TCI indicate that mafic-ultramafic rock and quartz-rich rock can be well extracted from other surface objects when interference from vegetation is lower. On account of the complexity of environmental systems, three periods of TCI were used to construct a three-dimensional scatter plot, named the multi-temporal thermal feature space (MTTFS) model. Then, the Bayes discriminant analysis algorithm was applied to the MTTFS model to extract rocks quantitatively. The classification accuracy of mafic-ultramafic rock is more than 75% in both training data and test data, which suggests TCI can act as a sensitive indicator to distinguish rocks and the MTTFS model can accurately extract mafic-ultramafic rock from other surface objects. We deduce that the use of thermodynamics is promising in lithology identification when an effective index is constructed and an appropriated model is selected.

Simultaneous retrieval of sea ice thickness and snow depth using concurrent active altimetry and passive L-band remote sensing data

NASA Astrophysics Data System (ADS)

Zhou, L.; Xu, S.; Liu, J.

2017-12-01

The retrieval of sea ice thickness mainly relies on satellite altimetry, and the freeboard measurements are converted to sea ice thickness (hi) under certain assumptions over snow loading. The uncertain in snow depth (hs) is a major source of uncertainty in the retrieved sea ice thickness and total volume for both radar and laser altimetry. In this study, novel algorithms for the simultaneous retrieval of hi and hs are proposed for the data synergy of L-band (1.4 GHz) passive remote sensing and both types of active altimetry: (1) L-band (1.4GHz) brightness temperature (TB) from Soil Moisture Ocean Salinity (SMOS) satellite and sea ice freeboard (FBice) from radar altimetry, (2) L-band TB data and snow freeboard (FBsnow) from laser altimetry. Two physical models serve as the forward models for the retrieval: L-band radiation model, and the hydrostatic equilibrium model. Verification with SMOS and Operational IceBridge (OIB) data is carried out, showing overall good retrieval accuracy for both sea ice parameters. Specifically, we show that the covariability between hs and FBsnow is crucial for the synergy between TB and FBsnow. Comparison with existing algorithms shows lower uncertainty in both sea ice parameters, and that the uncertainty in the retrieved sea ice thickness as caused by that of snow depth is spatially uncorrelated, with the potential reduction of the volume uncertainty through spatial sampling. The proposed algorithms can be applied to the retrieval of sea ice parameters at basin-scale, using concurrent active and passive remote sensing data based on satellites.

The Development, Validation and Use of the Rural and Remote Teaching, Working, Living and Learning Environment Survey (RRTWLLES)

ERIC Educational Resources Information Center

Dorman, Jeffrey; Kennedy, Joy; Young, Janelle

2015-01-01

Research in rural and remote schools and communities of Queensland resulted in the development and validation of the Rural and Remote Teaching, Working, Living and Learning Environment Survey (RRTWLLES). Samples of 252 teachers and 191 community members were used to validate the structure of this questionnaire. It was developed within the standard…

Normalized burn ratios link fire severity with patterns of avian occurrence

USGS Publications Warehouse

Rose, Eli T.; Simons, Theodore R.; Klein, Rob; McKerrow, Alexa

2016-01-01

ContextRemotely sensed differenced normalized burn ratios (DNBR) provide an index of fire severity across the footprint of a fire. We asked whether this index was useful for explaining patterns of bird occurrence within fire adapted xeric pine-oak forests of the southern Appalachian Mountains.ObjectivesWe evaluated the use of DNBR indices for linking ecosystem process with patterns of bird occurrence. We compared field-based and remotely sensed fire severity indices and used each to develop occupancy models for six bird species to identify patterns of bird occurrence following fire.MethodsWe identified and sampled 228 points within fires that recently burned within Great Smoky Mountains National Park. We performed avian point counts and field-assessed fire severity at each bird census point. We also used Landsat™ imagery acquired before and after each fire to quantify fire severity using DNBR. We used non-parametric methods to quantify agreement between fire severity indices, and evaluated single season occupancy models incorporating fire severity summarized at different spatial scales.ResultsAgreement between field-derived and remotely sensed measures of fire severity was influenced by vegetation type. Although occurrence models using field-derived indices of fire severity outperformed those using DNBR, summarizing DNBR at multiple spatial scales provided additional insights into patterns of occurrence associated with different sized patches of high severity fire.ConclusionsDNBR is useful for linking the effects of fire severity to patterns of bird occurrence, and informing how high severity fire shapes patterns of bird species occurrence on the landscape.

Development of a sampling method for the simultaneous monitoring of straight-chain alkanes, straight-chain saturated carbonyl compounds and monoterpenes in remote areas.

PubMed

Detournay, Anaïs; Sauvage, Stéphane; Locoge, Nadine; Gaudion, Vincent; Leonardis, Thierry; Fronval, Isabelle; Kaluzny, Pascal; Galloo, Jean-Claude

2011-04-01

Studies have shown that biogenic compounds, long chain secondary compounds and long lifetime anthropogenic compounds are involved in the formation of organic aerosols in both polluted areas and remote places. This work aims at developing an active sampling method to monitor these compounds (i.e. 6 straight-chain saturated aldehydes from C6 to C11; 8 straight-chain alkanes from C9 to C16; 6 monoterpenes: α-pinene, β-pinene, camphene, limonene, α-terpinene, & γ-terpinene; and 5 aromatic compounds: toluene, ethylbenzene, meta-, para- and ortho-xylenes) in remote areas. Samples are collected onto multi-bed sorbent cartridges at 200 mL min(-1) flow rate, using the automatic sampler SyPAC (TERA-Environnement, Crolles, France). No breakthrough was observed for sampling volumes up to 120 L (standard mixture at ambient temperature, with a relative humidity of 75%). As ozone has been shown to alter the samples (losses of 90% of aldehydes and up to 95% of terpenes were observed), the addition of a conditioned manganese dioxide (MnO(2)) scrubber to the system has been validated (full recovery of the affected compounds for a standard mixture at 50% relative humidity--RH). Samples are first thermodesorbed and then analysed by GC/FID/MS. This method allows suitable detection limits (from 2 ppt for camphene to 13 ppt for octanal--36 L sampled), and reproducibility (from 1% for toluene to 22% for heptanal). It has been successfully used to determine the diurnal variation of the target compounds (six 3 h samples a day) during winter and summer measurement campaigns at a remote site in the south of France.

A microfluidic device for dry sample preservation in remote settings.

PubMed

Begolo, Stefano; Shen, Feng; Ismagilov, Rustem F

2013-11-21

This paper describes a microfluidic device for dry preservation of biological specimens at room temperature that incorporates chemical stabilization matrices. Long-term stabilization of samples is crucial for remote medical analysis, biosurveillance, and archiving, but the current paradigm for transporting remotely obtained samples relies on the costly "cold chain" to preserve analytes within biospecimens. We propose an alternative approach that involves the use of microfluidics to preserve samples in the dry state with stabilization matrices, developed by others, that are based on self-preservation chemistries found in nature. We describe a SlipChip-based device that allows minimally trained users to preserve samples with the three simple steps of placing a sample at an inlet, closing a lid, and slipping one layer of the device. The device fills automatically, and a pre-loaded desiccant dries the samples. Later, specimens can be rehydrated and recovered for analysis in a laboratory. This device is portable, compact, and self-contained, so it can be transported and operated by untrained users even in limited-resource settings. Features such as dead-end and sequential filling, combined with a "pumping lid" mechanism, enable precise quantification of the original sample's volume while avoiding overfilling. In addition, we demonstrated that the device can be integrated with a plasma filtration module, and we validated device operations and capabilities by testing the stability of purified RNA solutions. These features and the modularity of this platform (which facilitates integration and simplifies operation) would be applicable to other microfluidic devices beyond this application. We envision that as the field of stabilization matrices develops, microfluidic devices will be useful for cost-effectively facilitating remote analysis and biosurveillance while also opening new opportunities for diagnostics, drug development, and other medical fields.

Multiple scattering in the remote sensing of natural surfaces

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

Li, Wen-Hao; Weeks, R.; Gillespie, A.R.

1996-07-01

Radiosity models predict the amount of light scattered many times (multiple scattering) among scene elements in addition to light interacting with a surface only once (direct reflectance). Such models are little used in remote sensing studies because they require accurate digital terrain models and, typically, large amounts of computer time. We have developed a practical radiosity model that runs relatively quickly within suitable accuracy limits, and have used it to explore problems caused by multiple-scattering in image calibration, terrain correction, and surface roughness estimation for optical images. We applied the radiosity model to real topographic surfaces sampled at two verymore » different spatial scales: 30 m (rugged mountains) and 1 cm (cobbles and gravel on an alluvial fan). The magnitude of the multiple-scattering (MS) effect varies with solar illumination geometry, surface reflectivity, sky illumination and surface roughness. At the coarse scale, for typical illumination geometries, as much as 20% of the image can be significantly affected (>5%) by MS, which can account for as much as {approximately}10% of the radiance from sunlit slopes, and much more for shadowed slopes, otherwise illuminated only by skylight. At the fine scale, radiance from as much as 30-40% of the scene can have a significant MS component, and the MS contribution is locally as high as {approximately}70%, although integrating to the meter scale reduces this limit to {approximately}10%. Because the amount of MS increases with reflectivity as well as roughness, MS effects will distort the shape of reflectance spectra as well as changing their overall amplitude. The change is proportional to surface roughness. Our results have significant implications for determining reflectivity and surface roughness in remote sensing.« less

Application of High Resolution Air-Borne Remote Sensing Observations for Monitoring NOx Emissions

NASA Astrophysics Data System (ADS)

Souri, A.; Choi, Y.; Pan, S.; Curci, G.; Janz, S. J.; Kowalewski, M. G.; Liu, J.; Herman, J. R.; Weinheimer, A. J.

2017-12-01

Nitrogen oxides (NOx=NO+NO2) are one of the air pollutants, responsible for the formation of tropospheric ozone, acid rain and particulate nitrate. The anthropogenic NOx emissions are commonly estimated based on bottom-up inventories which are complicated by many potential sources of error. One way to improve the emission inventories is to use relevant observations to constrain them. Fortunately, Nitrogen dioxide (NO2) is one of the most successful detected species from remote sensing. Although many studies have shown the capability of using space-borne remote sensing observations for monitoring emissions, the insufficient sample number and footprint of current measurements have introduced a burden to constrain emissions at fine scales. Promisingly, there are several air-borne sensors collected for NASA's campaigns providing high spatial resolution of NO2 columns. Here, we use the well-characterized NO2 columns from the Airborne Compact Atmospheric Mapper (ACAM) onboard NASA's B200 aircraft into a 1×1 km regional model to constrain anthropogenic NOx emissions in the Houston-Galveston-Brazoria area. Firstly, in order to incorporate the data, we convert the NO2 slant column densities to vertical ones using a joint of a radiative transfer model and the 1x1 km regional model constrained by P3-B aircraft measurements. After conducting an inverse modeling method using the Kalman filter, we find the ACAM observations are resourceful at mitigating the overprediction of model in reproducing NO2 on regular days. Moreover, the ACAM provides a unique opportunity to detect an anomaly in emissions leading to strong air quality degradation that is lacking in previous works. Our study provides convincing evidence that future geostationary satellites with high spatial and temporal resolutions will give us insights into uncertainties associated with the emissions at regional scales.

Fluid sampling apparatus and method

DOEpatents

Yeamans, D.R.

1998-02-03

Incorporation of a bellows in a sampling syringe eliminates ingress of contaminants, permits replication of amounts and compression of multiple sample injections, and enables remote sampling for off-site analysis. 3 figs.

Spatial probabilistic pulsatility model for enhancing photoplethysmographic imaging systems

NASA Astrophysics Data System (ADS)

Amelard, Robert; Clausi, David A.; Wong, Alexander

2016-11-01

Photoplethysmographic imaging (PPGI) is a widefield noncontact biophotonic technology able to remotely monitor cardiovascular function over anatomical areas. Although spatial context can provide insight into physiologically relevant sampling locations, existing PPGI systems rely on coarse spatial averaging with no anatomical priors for assessing arterial pulsatility. Here, we developed a continuous probabilistic pulsatility model for importance-weighted blood pulse waveform extraction. Using a data-driven approach, the model was constructed using a 23 participant sample with a large demographic variability (11/12 female/male, age 11 to 60 years, BMI 16.4 to 35.1 kg·m-2). Using time-synchronized ground-truth blood pulse waveforms, spatial correlation priors were computed and projected into a coaligned importance-weighted Cartesian space. A modified Parzen-Rosenblatt kernel density estimation method was used to compute the continuous resolution-agnostic probabilistic pulsatility model. The model identified locations that consistently exhibited pulsatility across the sample. Blood pulse waveform signals extracted with the model exhibited significantly stronger temporal correlation (W=35,p<0.01) and spectral SNR (W=31,p<0.01) compared to uniform spatial averaging. Heart rate estimation was in strong agreement with true heart rate [r2=0.9619, error (μ,σ)=(0.52,1.69) bpm].

Estimation of Soil Moisture Profile using a Simple Hydrology Model and Passive Microwave Remote Sensing

NASA Technical Reports Server (NTRS)

Soman, Vishwas V.; Crosson, William L.; Laymon, Charles; Tsegaye, Teferi

1998-01-01

Soil moisture is an important component of analysis in many Earth science disciplines. Soil moisture information can be obtained either by using microwave remote sensing or by using a hydrologic model. In this study, we combined these two approaches to increase the accuracy of profile soil moisture estimation. A hydrologic model was used to analyze the errors in the estimation of soil moisture using the data collected during Huntsville '96 microwave remote sensing experiment in Huntsville, Alabama. Root mean square errors (RMSE) in soil moisture estimation increase by 22% with increase in the model input interval from 6 hr to 12 hr for the grass-covered plot. RMSEs were reduced for given model time step by 20-50% when model soil moisture estimates were updated using remotely-sensed data. This methodology has a potential to be employed in soil moisture estimation using rainfall data collected by a space-borne sensor, such as the Tropical Rainfall Measuring Mission (TRMM) satellite, if remotely-sensed data are available to update the model estimates.

Remote information service access system based on a client-server-service model

DOEpatents

Konrad, Allan M.

1996-01-01

A local host computing system, a remote host computing system as connected by a network, and service functionalities: a human interface service functionality, a starter service functionality, and a desired utility service functionality, and a Client-Server-Service (CSS) model is imposed on each service functionality. In one embodiment, this results in nine logical components and three physical components (a local host, a remote host, and an intervening network), where two of the logical components are integrated into one Remote Object Client component, and that Remote Object Client component and the other seven logical components are deployed among the local host and remote host in a manner which eases compatibility and upgrade problems, and provides an illusion to a user that a desired utility service supported on a remote host resides locally on the user's local host, thereby providing ease of use and minimal software maintenance for users of that remote service.

Remote information service access system based on a client-server-service model

DOEpatents

Konrad, A.M.

1997-12-09

A local host computing system, a remote host computing system as connected by a network, and service functionalities: a human interface service functionality, a starter service functionality, and a desired utility service functionality, and a Client-Server-Service (CSS) model is imposed on each service functionality. In one embodiment, this results in nine logical components and three physical components (a local host, a remote host, and an intervening network), where two of the logical components are integrated into one Remote Object Client component, and that Remote Object Client component and the other seven logical components are deployed among the local host and remote host in a manner which eases compatibility and upgrade problems, and provides an illusion to a user that a desired utility service supported on a remote host resides locally on the user`s local host, thereby providing ease of use and minimal software maintenance for users of that remote service. 16 figs.

Remote information service access system based on a client-server-service model

DOEpatents

Konrad, Allan M.

1999-01-01

A local host computing system, a remote host computing system as connected by a network, and service functionalities: a human interface service functionality, a starter service functionality, and a desired utility service functionality, and a Client-Server-Service (CSS) model is imposed on each service functionality. In one embodiment, this results in nine logical components and three physical components (a local host, a remote host, and an intervening network), where two of the logical components are integrated into one Remote Object Client component, and that Remote Object Client component and the other seven logical components are deployed among the local host and remote host in a manner which eases compatibility and upgrade problems, and provides an illusion to a user that a desired utility service supported on a remote host resides locally on the user's local host, thereby providing ease of use and minimal software maintenance for users of that remote service.

Remote information service access system based on a client-server-service model

DOEpatents

Konrad, A.M.

1996-08-06

A local host computing system, a remote host computing system as connected by a network, and service functionalities: a human interface service functionality, a starter service functionality, and a desired utility service functionality, and a Client-Server-Service (CSS) model is imposed on each service functionality. In one embodiment, this results in nine logical components and three physical components (a local host, a remote host, and an intervening network), where two of the logical components are integrated into one Remote Object Client component, and that Remote Object Client component and the other seven logical components are deployed among the local host and remote host in a manner which eases compatibility and upgrade problems, and provides an illusion to a user that a desired utility service supported on a remote host resides locally on the user`s local host, thereby providing ease of use and minimal software maintenance for users of that remote service. 16 figs.

Remote information service access system based on a client-server-service model

DOEpatents

Konrad, Allan M.

1997-01-01

A local host computing system, a remote host computing system as connected by a network, and service functionalities: a human interface service functionality, a starter service functionality, and a desired utility service functionality, and a Client-Server-Service (CSS) model is imposed on each service functionality. In one embodiment, this results in nine logical components and three physical components (a local host, a remote host, and an intervening network), where two of the logical components are integrated into one Remote Object Client component, and that Remote Object Client component and the other seven logical components are deployed among the local host and remote host in a manner which eases compatibility and upgrade problems, and provides an illusion to a user that a desired utility service supported on a remote host resides locally on the user's local host, thereby providing ease of use and minimal software maintenance for users of that remote service.

Satellite remote sensing data can be used to model marine microbial metabolite turnover

PubMed Central

Larsen, Peter E; Scott, Nicole; Post, Anton F; Field, Dawn; Knight, Rob; Hamada, Yuki; Gilbert, Jack A

2015-01-01

Sampling ecosystems, even at a local scale, at the temporal and spatial resolution necessary to capture natural variability in microbial communities are prohibitively expensive. We extrapolated marine surface microbial community structure and metabolic potential from 72 16S rRNA amplicon and 8 metagenomic observations using remotely sensed environmental parameters to create a system-scale model of marine microbial metabolism for 5904 grid cells (49 km2) in the Western English Chanel, across 3 years of weekly averages. Thirteen environmental variables predicted the relative abundance of 24 bacterial Orders and 1715 unique enzyme-encoding genes that encode turnover of 2893 metabolites. The genes' predicted relative abundance was highly correlated (Pearson Correlation 0.72, P-value <10−6) with their observed relative abundance in sequenced metagenomes. Predictions of the relative turnover (synthesis or consumption) of CO2 were significantly correlated with observed surface CO2 fugacity. The spatial and temporal variation in the predicted relative abundances of genes coding for cyanase, carbon monoxide and malate dehydrogenase were investigated along with the predicted inter-annual variation in relative consumption or production of ∼3000 metabolites forming six significant temporal clusters. These spatiotemporal distributions could possibly be explained by the co-occurrence of anaerobic and aerobic metabolisms associated with localized plankton blooms or sediment resuspension, which facilitate the presence of anaerobic micro-niches. This predictive model provides a general framework for focusing future sampling and experimental design to relate biogeochemical turnover to microbial ecology. PMID:25072414

Satellite remote sensing data can be used to model marine microbial metabolite turnover

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

Larsen, Peter E.; Scott, Nicole; Post, Anton F.

Sampling ecosystems, even at a local scale, at the temporal and spatial resolution necessary to capture natural variability in microbial communities are prohibitively expensive. We extrapolated marine surface microbial community structure and metabolic potential from 72 16S rRNA amplicon and 8 metagenomic observations using remotely sensed environmental parameters to create a system-scale model of marine microbial metabolism for 5904 grid cells (49 km2) in the Western English Chanel, across 3 years of weekly averages. Thirteen environmental variables predicted the relative abundance of 24 bacterial Orders and 1715 unique enzyme-encoding genes that encode turnover of 2893 metabolites. The genes’ predicted relativemore » abundance was highly correlated (Pearson Correlation 0.72, P-value <10-6) with their observed relative abundance in sequenced metagenomes. Predictions of the relative turnover (synthesis or consumption) of CO2 were significantly correlated with observed surface CO2 fugacity. The spatial and temporal variation in the predicted relative abundances of genes coding for cyanase, carbon monoxide and malate dehydrogenase were investigated along with the predicted inter-annual variation in relative consumption or production of ~3000 metabolites forming six significant temporal clusters. These spatiotemporal distributions could possibly be explained by the co-occurrence of anaerobic and aerobic metabolisms associated with localized plankton blooms or sediment resuspension, which facilitate the presence of anaerobic micro-niches. This predictive model provides a general framework for focusing future sampling and experimental design to relate biogeochemical turnover to microbial ecology.« less

An in Situ Technique for Elemental Analysis of Lunar Surfaces

NASA Technical Reports Server (NTRS)

Kane, K. Y.; Cremers, D. A.

1992-01-01

An in situ analytical technique that can remotely determine the elemental constituents of solids has been demonstrated. Laser-Induced Breakdown Spectroscopy (LIBS) is a form of atomic emission spectroscopy in which a powerful laser pulse is focused on a solid to generate a laser spark, or microplasma. Material in the plasma is vaporized, and the resulting atoms are excited to emit light. The light is spectrally resolved to identify the emitting species. LIBS is a simple technique that can be automated for inclusion aboard a remotely operated vehicle. Since only optical access to a sample is required, areas inaccessible to a rover can be analyzed remotely. A single laser spark both vaporizes and excites the sample so that near real-time analysis (a few minutes) is possible. This technique provides simultaneous multielement detection and has good sensitivity for many elements. LIBS also eliminates the need for sample retrieval and preparation preventing possible sample contamination. These qualities make the LIBS technique uniquely suited for use in the lunar environment.

Single transmission line interrogated multiple channel data acquisition system

DOEpatents

Fasching, George E.; Keech, Jr., Thomas W.

1980-01-01

A single transmission line interrogated multiple channel data acquisition system is provided in which a plurality of remote station/sensor circuits each monitors a specific process variable and each transmits measurement values over a single transmission line to a master interrogating station when addressed by said master interrogating station. Typically, as many as 330 remote stations may be parallel connected to the transmission line which may exceed 7,000 feet. The interrogation rate is typically 330 stations/second. The master interrogating station samples each station according to a shared, charging transmit-receive cycle. All remote station address signals, all data signals from the remote stations/sensors and all power for all of the remote station/sensors are transmitted via a single continuous terminated coaxial cable. A means is provided for periodically and remotely calibrating all remote sensors for zero and span. A provision is available to remotely disconnect any selected sensor station from the main transmission line.

Comparison between remote sensing and a dynamic vegetation model for estimating terrestrial primary production of Africa.

PubMed

Ardö, Jonas

2015-12-01

Africa is an important part of the global carbon cycle. It is also a continent facing potential problems due to increasing resource demand in combination with climate change-induced changes in resource supply. Quantifying the pools and fluxes constituting the terrestrial African carbon cycle is a challenge, because of uncertainties in meteorological driver data, lack of validation data, and potentially uncertain representation of important processes in major ecosystems. In this paper, terrestrial primary production estimates derived from remote sensing and a dynamic vegetation model are compared and quantified for major African land cover types. Continental gross primary production estimates derived from remote sensing were higher than corresponding estimates derived from a dynamic vegetation model. However, estimates of continental net primary production from remote sensing were lower than corresponding estimates from the dynamic vegetation model. Variation was found among land cover classes, and the largest differences in gross primary production were found in the evergreen broadleaf forest. Average carbon use efficiency (NPP/GPP) was 0.58 for the vegetation model and 0.46 for the remote sensing method. Validation versus in situ data of aboveground net primary production revealed significant positive relationships for both methods. A combination of the remote sensing method with the dynamic vegetation model did not strongly affect this relationship. Observed significant differences in estimated vegetation productivity may have several causes, including model design and temperature sensitivity. Differences in carbon use efficiency reflect underlying model assumptions. Integrating the realistic process representation of dynamic vegetation models with the high resolution observational strength of remote sensing may support realistic estimation of components of the carbon cycle and enhance resource monitoring, providing suitable validation data is available.

Transport of Gas-Phase Anthropogenic VOCs to the Remote Troposphere During the NASA ATom Mission

NASA Astrophysics Data System (ADS)

Hornbrook, R. S.; Apel, E. C.; Hills, A. J.; Asher, E. C. C.; Emmons, L. K.; Blake, D. R.; Blake, N. J.; Simpson, I. J.; Barletta, B.; Meinardi, S.; Montzka, S. A.; Moore, F. L.; Miller, B. R.; Sweeney, C.; McKain, K.; Wofsy, S. C.; Daube, B. C.; Commane, R.; Bui, T. V.; Hanisco, T. F.; Wolfe, G. M.; St Clair, J. M.; Ryerson, T. B.; Thompson, C. R.; Peischl, J.; Ray, E. A.

2017-12-01

The NASA Atmospheric Tomography (ATom) project aims to study the impact of human-produced air pollution on greenhouse gases and on chemically reactive gases in the atmosphere. During the first two deployments, ATom-1 and ATom-2, which took place August 2016 and February 2017, respectively, a suite of trace gas measurement instruments were deployed on the NASA DC-8 which profiled the atmosphere between 0.2 and 13 km from near-pole to near-pole around the globe, sampling in the most remote regions of the atmosphere over the Arctic, Pacific, Southern, and Atlantic Oceans. Volatile organic compounds (VOCs) with a range of lifetimes from days to decades quantified using the Trace Organic Gas Analyzer (TOGA), Whole Air Sampler (WAS) and Programmable Flask Packages (PFPs) demonstrate a significant impact on the remote atmosphere from urban and industrial sources. Comparisons between the transport and fate of pollutants during Northern Hemisphere summer and winter will be presented. Observations of the distributions of anthropogenic VOCs will be compared with simulations using the Community Atmosphere Model with chemistry (CAM-chem).

Improving the Forecast Accuracy of an Ocean Observation and Prediction System by Adaptive Control of the Sensor Network

NASA Astrophysics Data System (ADS)

Talukder, A.; Panangadan, A. V.; Blumberg, A. F.; Herrington, T.; Georgas, N.

2008-12-01

The New York Harbor Observation and Prediction System (NYHOPS) is a real-time, estuarine and coastal ocean observing and modeling system for the New York Harbor and surrounding waters. Real-time measurements from in-situ mobile and stationary sensors in the NYHOPS networks are assimilated into marine forecasts in order to reduce the discrepancy with ground truth. The forecasts are obtained from the ECOMSED hydrodynamic model, a shallow water derivative of the Princeton Ocean Model. Currently, all sensors in the NYHOPS system are operated in a fixed mode with uniform sampling rates. This technology infusion effort demonstrates the use of Model Predictive Control (MPC) to autonomously adapt the operation of both mobile and stationary sensors in response to changing events that are -automatically detected from the ECOMSED forecasts. The controller focuses sensing resources on those regions that are expected to be impacted by the detected events. The MPC approach involves formulating the problem of calculating the optimal sensor parameters as a constrained multi-objective optimization problem. We have developed an objective function that takes into account the spatiotemporal relationship of the in-situ sensor locations and the locations of events detected by the model. Experiments in simulation were carried out using data collected during a freshwater flooding event. The location of the resulting freshwater plume was calculated from the corresponding model forecasts and was used by the MPC controller to derive control parameters for the sensing assets. The operational parameters that are controlled include the sampling rates of stationary sensors, paths of unmanned underwater vehicles (UUVs), and data transfer routes between sensors and the central modeling computer. The simulation experiments show that MPC-based sensor control reduces the RMS error in the forecast by a factor of 380% as compared to uniform sampling. The paths of multiple UUVs were simultaneously calculated such that measurements from on-board sensors would lead to maximal reduction in the forecast error after data assimilation. The MPC controller also reduces the consumption of system resources such as energy expended in sampling and wireless communication. The MPC-based control approach can be generalized to accept data from remote sensing satellites. This will enable in-situ sensors to be regulated using forecasts generated by assimilating local high resolution in-situ measurements with wide-area observations from remote sensing satellites.

Leaf Surface Effects on Retrieving Chlorophyll Content from Hyperspectral Remote Sensing

NASA Astrophysics Data System (ADS)

Qiu, Feng; Chen, JingMing; Ju, Weimin; Wang, Jun; Zhang, Qian

2017-04-01

Light reflected directly from the leaf surface without entering the surface layer is not influenced by leaf internal biochemical content. Leaf surface reflectance varies from leaf to leaf due to differences in the surface roughness features and is relatively more important in strong absorption spectral regions. Therefore it introduces dispersion of data points in the relationship between biochemical concentration and reflectance (especially in the visible region). Separation of surface from total leaf reflection is important to improve the link between leaf pigments content and remote sensing data. This study aims to estimate leaf surface reflectance from hyperspectral remote sensing data and retrieve chlorophyll content by inverting a modified PROSPECT model. Considering leaf surface reflectance is almost the same in the visible and near infrared spectral regions, a surface layer with a reflectance independent of wavelength but varying from leaf to leaf was added to the PROSPECT model. The specific absorption coefficients of pigments were recalibrated. Then the modified model was inverted on independent datasets to check the performance of the model in predicting the chlorophyll content. Results show that differences in estimated surface layer reflectance of various species are noticeable. Surface reflectance of leaves with epicuticular waxes and trichomes is usually higher than other samples. Reconstruction of leaf reflectance and transmittance in the 400-1000 nm wavelength region using the modified PROSPECT model is excellent with low root mean square error (RMSE) and bias. Improvements for samples with high surface reflectance (e.g. maize) are significant, especially for high pigment leaves. Moreover, chlorophyll retrieved from inversion of the modified model is consequently improved (RMSE from 5.9-13.3 ug/cm2 with mean value 8.1 ug/cm2, while mean correlation coefficient is 0.90) compared to results of PROSPECT-5 (RMSE from 9.6-20.2 ug/cm2 with mean value 13.1 ug/cm2, while mean correlation coefficient is 0.81). Underestimation of high chlorophyll content, which is due to underestimation of reflectance in the visible region of PROSPECT, is partially corrected or alleviated. Improvements are particularly noticeable for leaves with high surface reflectance or high chlorophyll content, which both lead to large proportions of surface reflectance to the total leaf reflectance.

Integrated crystal mounting and alignment system for high-throughput biological crystallography

DOEpatents

Nordmeyer, Robert A.; Snell, Gyorgy P.; Cornell, Earl W.; Kolbe, William F.; Yegian, Derek T.; Earnest, Thomas N.; Jaklevich, Joseph M.; Cork, Carl W.; Santarsiero, Bernard D.; Stevens, Raymond C.

2007-09-25

A method and apparatus for the transportation, remote and unattended mounting, and visual alignment and monitoring of protein crystals for synchrotron generated x-ray diffraction analysis. The protein samples are maintained at liquid nitrogen temperatures at all times: during shipment, before mounting, mounting, alignment, data acquisition and following removal. The samples must additionally be stably aligned to within a few microns at a point in space. The ability to accurately perform these tasks remotely and automatically leads to a significant increase in sample throughput and reliability for high-volume protein characterization efforts. Since the protein samples are placed in a shipping-compatible layered stack of sample cassettes each holding many samples, a large number of samples can be shipped in a single cryogenic shipping container.

Integrated crystal mounting and alignment system for high-throughput biological crystallography

DOEpatents

Nordmeyer, Robert A.; Snell, Gyorgy P.; Cornell, Earl W.; Kolbe, William; Yegian, Derek; Earnest, Thomas N.; Jaklevic, Joseph M.; Cork, Carl W.; Santarsiero, Bernard D.; Stevens, Raymond C.

2005-07-19

A method and apparatus for the transportation, remote and unattended mounting, and visual alignment and monitoring of protein crystals for synchrotron generated x-ray diffraction analysis. The protein samples are maintained at liquid nitrogen temperatures at all times: during shipment, before mounting, mounting, alignment, data acquisition and following removal. The samples must additionally be stably aligned to within a few microns at a point in space. The ability to accurately perform these tasks remotely and automatically leads to a significant increase in sample throughput and reliability for high-volume protein characterization efforts. Since the protein samples are placed in a shipping-compatible layered stack of sample cassettes each holding many samples, a large number of samples can be shipped in a single cryogenic shipping container.

Progress in remote sensing of global land surface heat fluxes and evaporations with a turbulent heat exchange parameterization method

NASA Astrophysics Data System (ADS)

Chen, Xuelong; Su, Bob

2017-04-01

Remote sensing has provided us an opportunity to observe Earth land surface with a much higher resolution than any of GCM simulation. Due to scarcity of information for land surface physical parameters, up-to-date GCMs still have large uncertainties in the coupled land surface process modeling. One critical issue is a large amount of parameters used in their land surface models. Thus remote sensing of land surface spectral information can be used to provide information on these parameters or assimilated to decrease the model uncertainties. Satellite imager could observe the Earth land surface with optical, thermal and microwave bands. Some basic Earth land surface status (land surface temperature, canopy height, canopy leaf area index, soil moisture etc.) has been produced with remote sensing technique, which already help scientists understanding Earth land and atmosphere interaction more precisely. However, there are some challenges when applying remote sensing variables to calculate global land-air heat and water exchange fluxes. Firstly, a global turbulent exchange parameterization scheme needs to be developed and verified, especially for global momentum and heat roughness length calculation with remote sensing information. Secondly, a compromise needs to be innovated to overcome the spatial-temporal gaps in remote sensing variables to make the remote sensing based land surface fluxes applicable for GCM model verification or comparison. A flux network data library (more 200 flux towers) was collected to verify the designed method. Important progress in remote sensing of global land flux and evaporation will be presented and its benefits for GCM models will also be discussed. Some in-situ studies on the Tibetan Plateau and problems of land surface process simulation will also be discussed.

Hydrological Relevant Parameters from Remote Sensing - Spatial Modelling Input and Validation Basis

NASA Astrophysics Data System (ADS)

Hochschild, V.

2012-12-01

This keynote paper will demonstrate how multisensoral remote sensing data is used as spatial input for mesoscale hydrological modeling as well as for sophisticated validation purposes. The tasks of Water Resources Management are subject as well as the role of remote sensing in regional catchment modeling. Parameters derived from remote sensing discussed in this presentation will be land cover, topographical information from digital elevation models, biophysical vegetation parameters, surface soil moisture, evapotranspiration estimations, lake level measurements, determination of snow covered area, lake ice cycles, soil erosion type, mass wasting monitoring, sealed area, flash flood estimation. The actual possibilities of recent satellite and airborne systems are discussed, as well as the data integration into GIS and hydrological modeling, scaling issues and quality assessment will be mentioned. The presentation will provide an overview of own research examples from Germany, Tibet and Africa (Ethiopia, South Africa) as well as other international research activities. Finally the paper gives an outlook on upcoming sensors and concludes the possibilities of remote sensing in hydrology.

Evaluation of the Consistency among In Situ and Remote Sensing Measurements of CO2 over North America using the CarbonTracker-Lagrange Regional Inverse Modeling Framework

NASA Astrophysics Data System (ADS)

Andrews, A. E.; Trudeau, M.; Hu, L.; Thoning, K. W.; Shiga, Y. P.; Michalak, A. M.; Benmergui, J. S.; Mountain, M. E.; Nehrkorn, T.; O'Dell, C.; Jacobson, A. R.; Miller, J.; Sweeney, C.; Chen, H.; Ploeger, F.; Tans, P. P.

2017-12-01

CarbonTracker-Lagrange (CT-L) is a regional inverse modeling system for estimating CO2 fluxes with rigorous uncertainty quantification. CT-L uses footprints from the Stochastic Time-Inverted Lagrangian Transport (STILT) model driven by high-resolution (10 to 30 km) meteorological fields from the Weather Research and Forecasting (WRF) model. We have computed a library of footprints corresponding to in situ and remote sensing measurements of CO2 over North America for 2007-2015. GOSAT and OCO-2 XCO2 retrievals are simulated using a suite of CT-L terrestrial ecosystem flux estimates that have been optimized with respect to in situ atmospheric CO2 measurements along with fossil fuel fluxes from emissions inventories. A vertical profile of STILT-WRF footprints was constructed corresponding to each simulated satellite retrieval, and CO2 profiles are generated by convolving the footprints with fluxes and attaching initial values advected from the domain boundaries. The stratospheric contribution to XCO2 has been estimated using 4-dimensional CO2 fields from the NOAA CarbonTracker model (version CT2016) and from the Chemical Lagrangian Model of the Stratosphere (CLaMS), after scaling the model fields to match data from the NOAA AirCore surface-to-stratosphere air sampling system. Tropospheric lateral boundary conditions are from CT2016 and from an empirical boundary value product derived from aircraft and marine boundary layer data. The averaging kernel and a priori CO2 profile are taken into account for direct comparisons with retrievals. We have focused on North America due to the relatively dense in situ measurements available with the aim of developing strategies for combined assimilation of in situ and remote sensing data. We will consider the extent to which interannual variability in terrestrial fluxes is manifest in the real and simulated satellite retrievals, and we will investigate possible systematic biases in the satellite retrievals and in the model.

Assessing map accuracy in a remotely sensed, ecoregion-scale cover map

USGS Publications Warehouse

Edwards, T.C.; Moisen, Gretchen G.; Cutler, D.R.

1998-01-01

Landscape- and ecoregion-based conservation efforts increasingly use a spatial component to organize data for analysis and interpretation. A challenge particular to remotely sensed cover maps generated from these efforts is how best to assess the accuracy of the cover maps, especially when they can exceed 1000 s/km2 in size. Here we develop and describe a methodological approach for assessing the accuracy of large-area cover maps, using as a test case the 21.9 million ha cover map developed for Utah Gap Analysis. As part of our design process, we first reviewed the effect of intracluster correlation and a simple cost function on the relative efficiency of cluster sample designs to simple random designs. Our design ultimately combined clustered and subsampled field data stratified by ecological modeling unit and accessibility (hereafter a mixed design). We next outline estimation formulas for simple map accuracy measures under our mixed design and report results for eight major cover types and the three ecoregions mapped as part of the Utah Gap Analysis. Overall accuracy of the map was 83.2% (SE=1.4). Within ecoregions, accuracy ranged from 78.9% to 85.0%. Accuracy by cover type varied, ranging from a low of 50.4% for barren to a high of 90.6% for man modified. In addition, we examined gains in efficiency of our mixed design compared with a simple random sample approach. In regard to precision, our mixed design was more precise than a simple random design, given fixed sample costs. We close with a discussion of the logistical constraints facing attempts to assess the accuracy of large-area, remotely sensed cover maps.

Effects of 4D-Var data assimilation using remote sensing precipitation products in a WRF over the complex Heihe River Basin

NASA Astrophysics Data System (ADS)

Pan, Xiaoduo; Li, Xin; Cheng, Guodong

2017-04-01

Traditionally, ground-based, in situ observations, remote sensing, and regional climate modeling, individually, cannot provide the high-quality precipitation data required for hydrological prediction, especially over complex terrain. Data assimilation techniques are often used to assimilate ground observations and remote sensing products into models for dynamic downscaling. In this study, the Weather Research and Forecasting (WRF) model was used to assimilate two satellite precipitation products (TRMM 3B42 and FY-2D) using the 4D-Var data assimilation method. The results show that the assimilation of remote sensing precipitation products can improve the initial WRF fields of humidity and temperature, thereby improving precipitation forecasting and decreasing the spin-up time. Hence, assimilating TRMM and FY-2D remote sensing precipitation products using WRF 4D-Var can be viewed as a positive step toward improving the accuracy and lead time of numerical weather prediction models, particularly for short-term weather forecasting. Future work is proposed to assimilate a suite of remote sensing data, e.g., the combination of precipitation and soil moisture data, into a WRF model to improve 7-8 day forecasts of precipitation and other atmospheric variables.

76 FR 43738 - Self-Regulatory Organizations; NASDAQ OMX PHLX LLC; Notice of Filing and Immediate Effectiveness...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-21

... filed an immediately effective proposal regarding Remote Specialists (the ``Remote Specialist filing'') that expanded the Remote Specialist concept.\\3\\ By the Remote Specialist filing, the Exchange enhanced the existing Remote Specialist \\4\\ model so that all eligible ROTs \\5\\ on the Exchange could function...

Mapping Plant Diversity and Composition Across North Carolina Piedmont Forest Landscapes Using Lidar-Hyperspectral Remote Sensing

NASA Astrophysics Data System (ADS)

Hakkenberg, Christopher R.

Forest modification, from local stress to global change, has given rise to efforts to model, map, and monitor critical properties of forest communities like structure, composition, and diversity. Predictive models based on data from spatially-nested field plots and LiDAR-hyperspectral remote sensing systems are one particularly effective means towards the otherwise prohibitively resource-intensive task of consistently characterizing forest community dynamics at landscape scales. However, to date, most predictive models fail to account for actual (rather than idealized) species and community distributions, are unsuccessful in predicting understory components in structurally and taxonomically heterogeneous forests, and may suffer from diminished predictive accuracy due to incongruity in scale and precision between field plot samples, remotely-sensed data, and target biota of varying size and density. This three-part study addresses these and other concerns in the modeling and mapping of emergent properties of forest communities by shifting the scope of prediction from the individual or taxon to the whole stand or community. It is, after all, at the stand scale where emergent properties like functional processes, biodiversity, and habitat aggregate and manifest. In the first study, I explore the relationship between forest structure (a proxy for successional demographics and resource competition) and tree species diversity in the North Carolina Piedmont, highlighting the empirical basis and potential for utilizing forest structure from LiDAR in predictive models of tree species diversity. I then extend these conclusions to map landscape pattern in multi-scale vascular plant diversity as well as turnover in community-continua at varying compositional resolutions in a North Carolina Piedmont landscape using remotely-sensed LiDAR-hyperspectral estimates of topography, canopy structure, and foliar biochemistry. Recognizing that the distinction between correlation and causation mirrors that between knowledge and understanding, all three studies distinguish between prediction of pattern and inference of process. Thus, in addition to advancing mapping methodologies relevant to a range of forest ecosystem management and monitoring applications, all three studies are noteworthy for assessing the ecological relationship between environmental predictors and emergent landscape patterns in plant composition and diversity in North Carolina Piedmont forests.

Evaluating high temporal and spatial resolution vegetation index for crop yield prediction

USDA-ARS?s Scientific Manuscript database

Remote sensing data have been widely used in estimating crop yield. Remote sensing derived parameters such as Vegetation Index (VI) were used either directly in building empirical models or by assimilating with crop growth models to predict crop yield. The abilities of remote sensing VI in crop yiel...

Remotely monitoring evaporation rate and soil water status using thermal imaging and "three-temperatures model (3T Model)" under field-scale conditions.

PubMed

Qiu, Guo Yu; Zhao, Ming

2010-03-01

Remote monitoring of soil evaporation and soil water status is necessary for water resource and environment management. Ground based remote sensing can be the bridge between satellite remote sensing and ground-based point measurement. The primary object of this study is to provide an algorithm to estimate evaporation and soil water status by remote sensing and to verify its accuracy. Observations were carried out in a flat field with varied soil water content. High-resolution thermal images were taken with a thermal camera; soil evaporation was measured with a weighing lysimeter; weather data were recorded at a nearby meteorological station. Based on the thermal imaging and the three-temperatures model (3T model), we developed an algorithm to estimate soil evaporation and soil water status. The required parameters of the proposed method were soil surface temperature, air temperature, and solar radiation. By using the proposed method, daily variation in soil evaporation was estimated. Meanwhile, soil water status was remotely monitored by using the soil evaporation transfer coefficient. Results showed that the daily variation trends of measured and estimated evaporation agreed with each other, with a regression line of y = 0.92x and coefficient of determination R(2) = 0.69. The simplicity of the proposed method makes the 3T model a potentially valuable tool for remote sensing.

Bushland Evapotranspiration and Agricultural Remote Sensing System (BEARS) software

NASA Astrophysics Data System (ADS)

Gowda, P. H.; Moorhead, J.; Brauer, D. K.

2017-12-01

Evapotranspiration (ET) is a major component of the hydrologic cycle. ET data are used for a variety of water management and research purposes such as irrigation scheduling, water and crop modeling, streamflow, water availability, and many more. Remote sensing products have been widely used to create spatially representative ET data sets which provide important information from field to regional scales. As UAV capabilities increase, remote sensing use is likely to also increase. For that purpose, scientists at the USDA-ARS research laboratory in Bushland, TX developed the Bushland Evapotranspiration and Agricultural Remote Sensing System (BEARS) software. The BEARS software is a Java based software that allows users to process remote sensing data to generate ET outputs using predefined models, or enter custom equations and models. The capability to define new equations and build new models expands the applicability of the BEARS software beyond ET mapping to any remote sensing application. The software also includes an image viewing tool that allows users to visualize outputs, as well as draw an area of interest using various shapes. This software is freely available from the USDA-ARS Conservation and Production Research Laboratory website.

The determinations of remote sensing satellite data delivery service quality: A positivistic case study in Chinese context

NASA Astrophysics Data System (ADS)

Jin, Jiahua; Yan, Xiangbin; Tan, Qiaoqiao; Li, Yijun

2014-03-01

With the development of remote sensing technology, remote-sensing satellite has been widely used in many aspects of national construction. Big data with different standards and massive users with different needs, make the satellite data delivery service to be a complex giant system. How to deliver remote-sensing satellite data efficiently and effectively is a big challenge. Based on customer service theory, this paper proposes a hierarchy conceptual model for examining the determinations of remote-sensing satellite data delivery service quality in the Chinese context. Three main dimensions: service expectation, service perception and service environment, and 8 sub-dimensions are included in the model. Large amount of first-hand data on the remote-sensing satellite data delivery service have been obtained through field research, semi-structured questionnaire and focused interview. A positivist case study is conducted to validate and develop the proposed model, as well as to investigate the service status and related influence mechanisms. Findings from the analysis demonstrate the explanatory validity of the model, and provide potentially helpful insights for future practice.

Combining Hydrological Modeling and Remote Sensing Observations to Enable Data-Driven Decision Making for Devils Lake Flood Mitigation in a Changing Climate

NASA Technical Reports Server (NTRS)

Zhang, Xiaodong; Kirilenko, Andrei; Lim, Howe; Teng, Williams

2010-01-01

This slide presentation reviews work to combine the hydrological models and remote sensing observations to monitor Devils Lake in North Dakota, to assist in flood damage mitigation. This reports on the use of a distributed rainfall-runoff model, HEC-HMS, to simulate the hydro-dynamics of the lake watershed, and used NASA's remote sensing data, including the TRMM Multi-Satellite Precipitation Analysis (TMPA) and AIRS surface air temperature, to drive the model.

A Teacher's Introduction to Remote Sensing.

ERIC Educational Resources Information Center

Kirman, Joseph M.

1997-01-01

Defines remote sensing as the examination of something without touching it. Generally, this refers to satellite and aerial photographic images. Discusses how this technology and resulting knowledge can be integrated into geography classes. Includes a sample unit using images. (MJP)

Validation of adipose lipid content as a body condition index for polar bears

USGS Publications Warehouse

McKinney, Melissa A.; Atwood, Todd C.; Dietz, Rune; Sonne, Christian; Iverson, Sara J.; Peacock, Elizabeth

2014-01-01

Body condition is a key indicator of individual and population health. Yet, there is little consensus as to the most appropriate condition index (CI), and most of the currently used CIs have not been thoroughly validated and are logistically challenging. Adipose samples from large datasets of capture biopsied, remote biopsied, and harvested polar bears were used to validate adipose lipid content as a CI via tests of accuracy, precision, sensitivity, biopsy depth, and storage conditions and comparisons to established CIs, to measures of health and to demographic and ecological parameters. The lipid content analyses of even very small biopsy samples were highly accurate and precise, but results were influenced by tissue depth at which the sample was taken. Lipid content of capture biopsies and samples from harvested adult females was correlated with established CIs and/or conformed to expected biological variation and ecological changes. However, lipid content of remote biopsies was lower than capture biopsies and harvested samples, possibly due to lipid loss during dart retrieval. Lipid content CI is a biologically relevant, relatively inexpensive and rapidly assessed CI and can be determined routinely for individuals and populations in order to infer large-scale spatial and long-term temporal trends. As it is possible to collect samples during routine harvesting or remotely using biopsy darts, monitoring and assessment of body condition can be accomplished without capture and handling procedures or noninvasively, which are methods that are preferred by local communities. However, further work is needed to apply the method to remote biopsies.

Validation of adipose lipid content as a body condition index for polar bears

PubMed Central

McKinney, Melissa A; Atwood, Todd; Dietz, Rune; Sonne, Christian; Iverson, Sara J; Peacock, Elizabeth

2014-01-01

Body condition is a key indicator of individual and population health. Yet, there is little consensus as to the most appropriate condition index (CI), and most of the currently used CIs have not been thoroughly validated and are logistically challenging. Adipose samples from large datasets of capture biopsied, remote biopsied, and harvested polar bears were used to validate adipose lipid content as a CI via tests of accuracy, precision, sensitivity, biopsy depth, and storage conditions and comparisons to established CIs, to measures of health and to demographic and ecological parameters. The lipid content analyses of even very small biopsy samples were highly accurate and precise, but results were influenced by tissue depth at which the sample was taken. Lipid content of capture biopsies and samples from harvested adult females was correlated with established CIs and/or conformed to expected biological variation and ecological changes. However, lipid content of remote biopsies was lower than capture biopsies and harvested samples, possibly due to lipid loss during dart retrieval. Lipid content CI is a biologically relevant, relatively inexpensive and rapidly assessed CI and can be determined routinely for individuals and populations in order to infer large-scale spatial and long-term temporal trends. As it is possible to collect samples during routine harvesting or remotely using biopsy darts, monitoring and assessment of body condition can be accomplished without capture and handling procedures or noninvasively, which are methods that are preferred by local communities. However, further work is needed to apply the method to remote biopsies. PMID:24634735

Disposable remote zero headspace extractor

DOEpatents

Hand, Julie J [Idaho Falls, ID; Roberts, Mark P [Arco, ID

2006-03-21

The remote zero headspace extractor uses a sampling container inside a stainless steel vessel to perform toxicity characteristics leaching procedure to analyze volatile organic compounds. The system uses an in line filter for ease of replacement. This eliminates cleaning and disassembly of the extractor. All connections are made with quick connect fittings which can be easily replaced. After use, the bag can be removed and disposed of, and a new sampling container is inserted for the next extraction.

Deployment of a Reverse Transcription Loop-Mediated Isothermal Amplification Test for Ebola Virus Surveillance in Remote Areas in Guinea.

PubMed

Kurosaki, Yohei; Magassouba, N'Faly; Bah, Hadja Aïssatou; Soropogui, Barré; Doré, Amadou; Kourouma, Fodé; Cherif, Mahamoud Sama; Keita, Sakoba; Yasuda, Jiro

2016-10-15

To strengthen the laboratory diagnostic capacity for Ebola virus disease (EVD) in the remote areas of Guinea, we deployed a mobile field laboratory and implemented reverse transcription loop-mediated isothermal amplification (RT-LAMP) for postmortem testing. We tested 896 oral swab specimens and 21 serum samples, using both RT-LAMP and reverse transcription-polymerase chain reaction (RT-PCR). Neither test yielded a positive result, and the results from RT-LAMP and RT-PCR were consistent. More than 95% of the samples were tested within 2 days of sample collection. These results highlight the usefulness of the RT-LAMP assay as an EVD diagnostic testing method in the field or remote areas. © The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail journals.permissions@oup.com.

Polychlorinated dibenzo-p-dioxins and dibenzofurans in the remote North Atlantic marine atmosphere

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

Baker, J.I.; Hites, R.A.

1999-01-01

The authors have developed a sampling strategy that allows them to determine femtogram/cubic mater concentrations of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/F) in remote marine atmospheres. Using this sampling strategy, a total of 37 air samples were taken during two extended sampling periods at Bermuda between September 1993 and August 1997. During this time, the average total PCDD/F concentrations at Bermuda decreased from 105 {+-} 30 to 35 {+-} 10 fg/m{sup 3}, giving a half-life of about 2 years for these compounds in the remote marine atmosphere. PCDD/F concentrations during both sampling periods were somewhat higher in the winter thenmore » air parcels originated from North America. A second air-sampling station was established at Barbados where 22 air samples were taken between March 1996 and August 1997; an average total PCDD/F concentration at Barbados of 15 {+-} 7 fg/m{sup 3} was found. This value was not significantly different than the 27 {+-} 7 fg/m{sup 3} found at Bermuda during this time when air arrived from the east. This indicated that the remote marine background concentration for these compounds is currently on the order of 20 fg/m{sup 3}. Using these background concentrations, the dry depositional rate of PCDD/F to the world`s oceans was estimated to be 200 {+-} 80 kg/year, and the wet depositional rate was estimated to be 900 {+-} 300 kg/year. This is a total deposition rate of about 1 t/year to the oceans as compared to their previous estimate of 12 t/year PCDD/F deposition from the atmosphere to the land.« less

Central and peripheral information source use among rural and remote Registered Nurses.

PubMed

Kosteniuk, Julie G; D'Arcy, Carl; Stewart, Norma; Smith, Barbara

2006-07-01

This paper reports a study examining the use of central (colleagues, inservice and newsletters) and peripheral information sources (Internet, library, journal subscriptions and continuing education) among a large sample of rural and remote nurses and explores the factors associated with the use of particular peripheral information sources. There have been few studies of the specific sources of information accessed by Registered Nurses, particularly rural or remote nurses, and the characteristics of nurses and their organizations that are associated with the use of particular information sources. A questionnaire survey was conducted with 3933 Registered Nurses from all regions of rural and remote Canada between October 2001 and July 2002. We used frequencies and cross-tabulations to describe rates of information use, and forward selection logistic regression with likelihood ratio selection to build the best-fitting model of the variables that affected the odds of using each peripheral information source. Nursing colleagues ranked as the information source most frequently used, and the Internet and library ranked lowest. On average, nurses used a statistically significantly greater number of central than peripheral sources. Peripheral information source use was higher among nurses who had access to current information, opportunities to share their knowledge with others, higher education levels, were in positions of authority and worked with healthcare students. The associations between age and geographical location varied according to the peripheral information source under consideration. The vast majority of rural and remote nurses used at least one peripheral information source to inform their practice. Increasing the number of research sources used by these nurses requires attention to issues of information access in these areas, as well as issues of staff recruitment and retention of staff in under-serviced rural and remote regions.

[Monitoring the thermal plume from coastal nuclear power plant using satellite remote sensing data: modeling, and validation].

PubMed

Zhu, Li; Zhao, Li-Min; Wang, Qiao; Zhang, Ai-Ling; Wu, Chuan-Qing; Li, Jia-Guo; Shi, Ji-Xiang

2014-11-01

Thermal plume from coastal nuclear power plant is a small-scale human activity, mornitoring of which requires high-frequency and high-spatial remote sensing data. The infrared scanner (IRS), on board of HJ-1B, has an infrared channel IRS4 with 300 m and 4-days as its spatial and temporal resolution. Remote sensing data aquired using IRS4 is an available source for mornitoring thermal plume. Retrieval pattern for coastal sea surface temperature (SST) was built to monitor the thermal plume from nuclear power plant. The research area is located near Guangdong Daya Bay Nuclear Power Station (GNPS), where synchronized validations were also implemented. The National Centers for Environmental Prediction (NCEP) data was interpolated spatially and temporally. The interpolated data as well as surface weather conditions were subsequently employed into radiative transfer model for the atmospheric correction of IRS4 thermal image. A look-up-table (LUT) was built for the inversion between IRS4 channel radiance and radiometric temperature, and a fitted function was also built from the LUT data for the same purpose. The SST was finally retrieved based on those preprocessing procedures mentioned above. The bulk temperature (BT) of 84 samples distributed near GNPS was shipboard collected synchronically using salinity-temperature-deepness (CTD) instruments. The discrete sample data was surface interpolated and compared with the satellite retrieved SST. Results show that the average BT over the study area is 0.47 degrees C higher than the retrieved skin temperature (ST). For areas far away from outfall, the ST is higher than BT, with differences less than 1.0 degrees C. The main driving force for temperature variations in these regions is solar radiation. For areas near outfall, on the contrary, the retrieved ST is lower than BT, and greater differences between the two (meaning > 1.0 degrees C) happen when it gets closer to the outfall. Unlike the former case, the convective heat transfer resulting from the thermal plume is the primary reason leading to the temperature variations. Temperature rising (TR) distributions obtained from remote sensing data and in-situ measurements are consistent, except that the interpolated BT shows more level details (> 5 levels) than that of the ST (up to 4 levels). The areas with higher TR levels (> 2) are larger on BT maps, while for lower TR levels (≤ 2), the two methods perform with no obvious differences. Minimal errors for satellite-derived SST occur regularly around local time 10 a. m. This makes the remote sensing results to be substitutes for in-situ measurements. Therefore, for operational applications of HJ-1B IRS4, remote sensing technique can be a practical approach to monitoring the nuclear plant thermal pollution around this time period.

Autonomous water sampling for long-term monitoring of trace metals in remote environments.

PubMed

Kim, Hyojin; Bishop, James K B; Wood, Todd J; Fung, Inez Y

2012-10-16

A remotely controlled autonomous method for long-term high-frequency sampling of environmental waters in remote locations is described. The method which preserves sample integrity of dissolved trace metals and major ions for month-long periods employs a gravitational filtration system (GFS) that separates dissolved and particulate phases as samples are collected. The key elements of GFS are (1) a modified "air-outlet" filter holder to maximize filtration rate and thus minimize filtration artifacts; and (2) the direct delivery of filtrate to dedicated bottle sets for specific analytes. Depth and screen filter types were evaluated with depth filters showing best performance. GFS performance is validated using ground, stream, and estuary waters. Over 30 days of storage, samples with GFS treatment had average recoveries of 95 ± 19% and 105 ± 7% of Fe and Mn, respectively; without GFS treatment, average recoveries were only 16% and 18%. Dissolved major cations K, Mg, and Na were stable independent of collection methodology, whereas Ca in some groundwater samples decreased up to 42% without GFS due to CaCO(3) precipitation. In-field performance of GFS equipped autosamplers is demonstrated using ground and streamwater samples collected at the Angelo Coast Range Reserve, California from October 3 to November 4 2011.

Evaluating lake phytoplanton response to human disturbance and climate change using satellite imagery

NASA Astrophysics Data System (ADS)

Novitski, Linda Nicole

Accurate and cost-effective assessment of water quality is necessary for proper management and restoration of inland water bodies susceptible to algal bloom conditions. Landsat and MODIS satellite images were used to create chlorophyll and Secchi depth predictive models for algal assessment of Great Lakes and other lakes of the United States. Boosted regression tree (BRT) models using satellite imagery are both easy to use and can have high predictive performance. BRT models inferred chlorophyll and Secchi depth more accurately than linear regression models for all study locations. Inferred chlorophyll of inner Saginaw Bay was subsequently used in ecological models to help understand the ecological drivers of algal blooms in this ecosystem. For small lakes (non-Great Lakes), the best national Landsat model for ln-transformed chlorophyll was the BRT model and had a cross-validation R 2 of 0.44 and a 0.76 ln-transformed mug/L RMSE. The best national Landsat model for Secchi depth was also a BRT model that had an adjusted R 2 of 0.52 and a 0.80 m RMSE. We assessed the applicability of the national chlorophyll model for ecological analysis by comparing the total phosphorus- chlorophyll relationship with chlorophyll determined from sampling or remote sensing, which showed the total phosphorus- chlorophyll relationship had an adjusted R2 = 0.58 and 1.02 ln-transformed microg/L RMSE with sampled chlorophyll versus an adjusted R2 = 0.56 and 1.04 ln-transformed mug/L RMSE with chlorophyll determined by the boosted regression tree remote sensing model. For Great Lakes models, the MODIS BRT model predicted chlorophyll most accurately of the three BRT models and compared well to other models in the literature. BRT models for Landsat ETM+ and TM more accurately predicted chlorophyll than the MSS model and all Landsat models had favorable results when compared to the literature. BRT chlorophyll predictive models are useful in helping to understand historical, long-term chlorophyll trends and to inform us of how climate change may alter ecosystems in the future. In inner Saginaw Bay, annual average and upper quartile Landsat-derived chlorophyll decreased from 7.44 to 6.62 and 8.38 to 7.38 mug/L between 1973-1982, and annual upper quartile of 8-day phosphorus loads increased from 5.29 to 6.79 kg between 1973-2012. Simple linear and multiple regression models and Wilcoxon rank test results for MODIS and Landsat-derived chlorophyll indicate that distance from the Saginaw River mouth influences chlorophyll concentration in Saginaw Bay; Landsat-derived surface water temperature and phosphorus loads to a lesser extent. Mixed-effect models for MODIS and Landsat-derived chlorophyll were related to chlorophyll better than simple linear or multiple regressions, with random effects of pixel and sample date contributing substantially to predictive power (NSE=0.35-70), though phosphorus loads, distance to Saginaw River mouth, and water were significant fixed effects in most models. Water quality changes in Saginaw Bay between 1972-2012 were influenced by phosphorus loading and distance to the Saginaw River's mouth. Landsat and MODIS imagery are complementary platforms because of the long history of Landsat operation and the finer spectral resolution and image frequency of MODIS. Remote sensing water quality assessment tools can be valuable for limnological study, ecological assessment, and water resource management.

Combining remotely sensed and other measurements for hydrologic areal averages

NASA Technical Reports Server (NTRS)

Johnson, E. R.; Peck, E. L.; Keefer, T. N.

1982-01-01

A method is described for combining measurements of hydrologic variables of various sampling geometries and measurement accuracies to produce an estimated mean areal value over a watershed and a measure of the accuracy of the mean areal value. The method provides a means to integrate measurements from conventional hydrological networks and remote sensing. The resulting areal averages can be used to enhance a wide variety of hydrological applications including basin modeling. The correlation area method assigns weights to each available measurement (point, line, or areal) based on the area of the basin most accurately represented by the measurement. The statistical characteristics of the accuracy of the various measurement technologies and of the random fields of the hydrologic variables used in the study (water equivalent of the snow cover and soil moisture) required to implement the method are discussed.

Underwater lidar system: design challenges and application in pollution detection

NASA Astrophysics Data System (ADS)

Gupta, Pradip; Sankolli, Swati; Chakraborty, A.

2016-05-01

The present remote sensing techniques have imposed limitations in the applications of LIDAR Technology. The fundamental sampling inadequacy of the remote sensing data obtained from satellites is that they cannot resolve in the third spatial dimension, the vertical. This limits our possibilities of measuring any vertical variability in the water column. Also the interaction between the physical and biological process in the oceans and their effects at subsequent depths cannot be modeled with present techniques. The idea behind this paper is to introduce underwater LIDAR measurement system by using a LIDAR mounted on an Autonomous Underwater Vehicle (AUV). The paper introduces working principles and design parameters for the LIDAR mounted AUV (AUV-LIDAR). Among several applications the papers discusses the possible use and advantages of AUV-LIDAR in water pollution detection through profiling of Dissolved Organic Matter (DOM) in water bodies.

Intercomparison of Remotely Sensed Vegetation Indices, Ground Spectroscopy, and Foliar Chemistry Data from NEON

NASA Astrophysics Data System (ADS)

Hulslander, D.; Warren, J. N.; Weintraub, S. R.

2017-12-01

Hyperspectral imaging systems can be used to produce spectral reflectance curves giving rich information about composition, relative abundances of materials, mixes and combinations. Indices based on just a few spectral bands have been used for over 40 years to study vegetation health, mineral abundance, and more. These indices are much simpler to visualize and use than a full hyperspectral data set which may contain over 400 bands. Yet historically, it has been difficult to directly relate remotely sensed spectral indices to quantitative biophysical properties significant to forest ecology such as canopy nitrogen, lignin, and chlorophyll. This linkage is a critical piece in enabling the detection of high value ecological information, usually only available from labor-intensive canopy foliar chemistry sampling, to the geographic and temporal coverage available via remote sensing. Previous studies have shown some promising results linking ground-based data and remotely sensed indices, but are consistently limited in time, geographic extent, and land cover type. Moreover, previous studies are often focused on tuning linkage algorithms for the purpose of achieving good results for only one study site or one type of vegetation, precluding development of more generalized algorithms. The National Ecological Observatory Network (NEON) is a unique system of 47 terrestrial sites covering all of the major eco-climatic domains of the US, including AK, HI, and Puerto Rico. These sites are regularly monitored and sampled using uniform instrumentation and protocols, including both foliar chemistry sampling and remote sensing flights for high resolution hyperspectral, LiDAR, and digital camera data acquisition. In this study we compare the results of foliar chemistry analysis to the remote sensing vegetation indices and investigate possible sources for variance and difference through the use of the larger hyperspectral dataset as well as ground based spectrometer measurements of samples subsequently analyzed for foliar chemistry.

New developments in sampling and aggregation for remotely sensed surveys

NASA Technical Reports Server (NTRS)

Feiveson, A. H. (Principal Investigator)

1979-01-01

Sampling techniques used to construct large area crop estimates are briefly reviewed. Problem areas in sampling and aggregation are covered. The natural sampling strategy, two phase sampling, weighted aggregation, and multiyear estimation are among the topics discussed.

Using multi-level remote sensing and ground data to estimate forest biomass resources in remote regions: a case study in the boreal forests of interior Alaska

Treesearch

Hans-Erik Andersen; Strunk Jacob; Hailemariam Temesgen; Donald Atwood; Ken Winterberger

2012-01-01

The emergence of a new generation of remote sensing and geopositioning technologies, as well as increased capabilities in image processing, computing, and inferential techniques, have enabled the development and implementation of increasingly efficient and cost-effective multilevel sampling designs for forest inventory. In this paper, we (i) describe the conceptual...

Mathematic modeling of the Earth's surface and the process of remote sensing

NASA Technical Reports Server (NTRS)

Balter, B. M.

1979-01-01

It is shown that real data from remote sensing of the Earth from outer space are not best suited to the search for optimal procedures with which to process such data. To work out the procedures, it was proposed that data synthesized with the help of mathematical modeling be used. A criterion for simularity to reality was formulated. The basic principles for constructing methods for modeling the data from remote sensing are recommended. A concrete method is formulated for modeling a complete cycle of radiation transformations in remote sensing. A computer program is described which realizes the proposed method. Some results from calculations are presented which show that the method satisfies the requirements imposed on it.

Remote sensing sensors and applications in environmental resources mapping and modeling

USGS Publications Warehouse

Melesse, Assefa M.; Weng, Qihao; Thenkabail, Prasad S.; Senay, Gabriel B.

2007-01-01

The history of remote sensing and development of different sensors for environmental and natural resources mapping and data acquisition is reviewed and reported. Application examples in urban studies, hydrological modeling such as land-cover and floodplain mapping, fractional vegetation cover and impervious surface area mapping, surface energy flux and micro-topography correlation studies is discussed. The review also discusses the use of remotely sensed-based rainfall and potential evapotranspiration for estimating crop water requirement satisfaction index and hence provides early warning information for growers. The review is not an exhaustive application of the remote sensing techniques rather a summary of some important applications in environmental studies and modeling.

[Detecting the moisture content of forest surface soil based on the microwave remote sensing technology.

PubMed

Li, Ming Ze; Gao, Yuan Ke; Di, Xue Ying; Fan, Wen Yi

2016-03-01

The moisture content of forest surface soil is an important parameter in forest ecosystems. It is practically significant for forest ecosystem related research to use microwave remote sensing technology for rapid and accurate estimation of the moisture content of forest surface soil. With the aid of TDR-300 soil moisture content measuring instrument, the moisture contents of forest surface soils of 120 sample plots at Tahe Forestry Bureau of Daxing'anling region in Heilongjiang Province were measured. Taking the moisture content of forest surface soil as the dependent variable and the polarization decomposition parameters of C band Quad-pol SAR data as independent variables, two types of quantitative estimation models (multilinear regression model and BP-neural network model) for predicting moisture content of forest surface soils were developed. The spatial distribution of moisture content of forest surface soil on the regional scale was then derived with model inversion. Results showed that the model precision was 86.0% and 89.4% with RMSE of 3.0% and 2.7% for the multilinear regression model and the BP-neural network model, respectively. It indicated that the BP-neural network model had a better performance than the multilinear regression model in quantitative estimation of the moisture content of forest surface soil. The spatial distribution of forest surface soil moisture content in the study area was then obtained by using the BP neural network model simulation with the Quad-pol SAR data.

Equivalent Sensor Radiance Generation and Remote Sensing from Model Parameters. Part 1; Equivalent Sensor Radiance Formulation

NASA Technical Reports Server (NTRS)

Wind, Galina; DaSilva, Arlindo M.; Norris, Peter M.; Platnick, Steven E.

2013-01-01

In this paper we describe a general procedure for calculating equivalent sensor radiances from variables output from a global atmospheric forecast model. In order to take proper account of the discrepancies between model resolution and sensor footprint the algorithm takes explicit account of the model subgrid variability, in particular its description of the probably density function of total water (vapor and cloud condensate.) The equivalent sensor radiances are then substituted into an operational remote sensing algorithm processing chain to produce a variety of remote sensing products that would normally be produced from actual sensor output. This output can then be used for a wide variety of purposes such as model parameter verification, remote sensing algorithm validation, testing of new retrieval methods and future sensor studies. We show a specific implementation using the GEOS-5 model, the MODIS instrument and the MODIS Adaptive Processing System (MODAPS) Data Collection 5.1 operational remote sensing cloud algorithm processing chain (including the cloud mask, cloud top properties and cloud optical and microphysical properties products.) We focus on clouds and cloud/aerosol interactions, because they are very important to model development and improvement.

Multi-sensor Cloud Retrieval Simulator and Remote Sensing from Model Parameters . Pt. 1; Synthetic Sensor Radiance Formulation; [Synthetic Sensor Radiance Formulation

NASA Technical Reports Server (NTRS)

Wind, G.; DaSilva, A. M.; Norris, P. M.; Platnick, S.

2013-01-01

In this paper we describe a general procedure for calculating synthetic sensor radiances from variable output from a global atmospheric forecast model. In order to take proper account of the discrepancies between model resolution and sensor footprint, the algorithm takes explicit account of the model subgrid variability, in particular its description of the probability density function of total water (vapor and cloud condensate.) The simulated sensor radiances are then substituted into an operational remote sensing algorithm processing chain to produce a variety of remote sensing products that would normally be produced from actual sensor output. This output can then be used for a wide variety of purposes such as model parameter verification, remote sensing algorithm validation, testing of new retrieval methods and future sensor studies.We show a specific implementation using the GEOS-5 model, the MODIS instrument and the MODIS Adaptive Processing System (MODAPS) Data Collection 5.1 operational remote sensing cloud algorithm processing chain (including the cloud mask, cloud top properties and cloud optical and microphysical properties products). We focus on clouds because they are very important to model development and improvement.

The wildfire experiment (WIFE): observations with airborne remote sensors

Treesearch

L.F. Radke; T.L. Clark; J.L. Coen; C.A. Walther; R.N. Lockwood; P.J. Riggan; J.A. Brass; R.G. Higgins

2000-01-01

Airborne remote sensors have long been a cornerstone of wildland fire research, and recently three-dimensional fire behaviour models fully coupled to the atmosphere have begun to show a convincing level of verisimilitude. The WildFire Experiment (WiFE) attempted the marriage of airborne remote sensors, multi-sensor observations together with fire model development and...

The added value of remote sensing products in constraining hydrological models

NASA Astrophysics Data System (ADS)

Nijzink, Remko C.; Almeida, Susana; Pechlivanidis, Ilias; Capell, René; Gustafsson, David; Arheimer, Berit; Freer, Jim; Han, Dawei; Wagener, Thorsten; Sleziak, Patrik; Parajka, Juraj; Savenije, Hubert; Hrachowitz, Markus

2017-04-01

The calibration of a hydrological model still depends on the availability of streamflow data, even though more additional sources of information (i.e. remote sensed data products) have become more widely available. In this research, the model parameters of four different conceptual hydrological models (HYPE, HYMOD, TUW, FLEX) were constrained with remotely sensed products. The models were applied over 27 catchments across Europe to cover a wide range of climates, vegetation and landscapes. The fluxes and states of the models were correlated with the relevant products (e.g. MOD10A snow with modelled snow states), after which new a-posteriori parameter distributions were determined based on a weighting procedure using conditional probabilities. Briefly, each parameter was weighted with the coefficient of determination of the relevant regression between modelled states/fluxes and products. In this way, final feasible parameter sets were derived without the use of discharge time series. Initial results show that improvements in model performance, with regard to streamflow simulations, are obtained when the models are constrained with a set of remotely sensed products simultaneously. In addition, we present a more extensive analysis to assess a model's ability to reproduce a set of hydrological signatures, such as rising limb density or peak distribution. Eventually, this research will enhance our understanding and recommendations in the use of remotely sensed products for constraining conceptual hydrological modelling and improving predictive capability, especially for data sparse regions.

Scripps Ocean Modeling and Remote Sensing (SOMARS)

DTIC Science & Technology

1988-09-20

Topics in this brief reports include: Kalman filtering of oceanographic data; Remote sensing of sea surface temperature; Altimetry and Surface heat fluxes; Ocean models of the marine mixed layer; Radar altimetry; Mathematical model of California current eddies.

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. 

MiniSipper: A new in situ water sampler for high-resolution, long-duration acid mine drainage monitoring

USGS Publications Warehouse

Chapin, Thomas P.; Todd, Andrew S.

2012-01-01

Abandoned hard-rock mines can be a significant source of acid mine drainage (AMD) and toxic metal pollution to watersheds. In Colorado, USA, abandoned mines are often located in remote, high elevation areas that are snowbound for 7–8 months of the year. The difficulty in accessing these remote sites, especially during winter, creates challenging water sampling problems and major hydrologic and toxic metal loading events are often under sampled. Currently available automated water samplers are not well suited for sampling remote snowbound areas so the U.S. Geological Survey (USGS) has developed a new water sampler, the MiniSipper, to provide long-duration, high-resolution water sampling in remote areas. The MiniSipper is a small, portable sampler that uses gas bubbles to separate up to 250 five milliliter acidified samples in a long tubing coil. The MiniSipper operates for over 8 months unattended in water under snow/ice, reduces field work costs, and greatly increases sampling resolution, especially during inaccessible times. MiniSippers were deployed in support of an U.S. Environmental Protection Agency (EPA) project evaluating acid mine drainage inputs from the Pennsylvania Mine to the Snake River watershed in Summit County, CO, USA. MiniSipper metal results agree within 10% of EPA-USGS hand collected grab sample results. Our high-resolution results reveal very strong correlations (R2 > 0.9) between potentially toxic metals (Cd, Cu, and Zn) and specific conductivity at the Pennsylvania Mine site. The large number of samples collected by the MiniSipper over the entire water year provides a detailed look at the effects of major hydrologic events such as snowmelt runoff and rainstorms on metal loading from the Pennsylvania Mine. MiniSipper results will help guide EPA sampling strategy and remediation efforts in the Snake River watershed.

MiniSipper: a new in situ water sampler for high-resolution, long-duration acid mine drainage monitoring.

PubMed

Chapin, Thomas P; Todd, Andrew S

2012-11-15

Abandoned hard-rock mines can be a significant source of acid mine drainage (AMD) and toxic metal pollution to watersheds. In Colorado, USA, abandoned mines are often located in remote, high elevation areas that are snowbound for 7-8 months of the year. The difficulty in accessing these remote sites, especially during winter, creates challenging water sampling problems and major hydrologic and toxic metal loading events are often under sampled. Currently available automated water samplers are not well suited for sampling remote snowbound areas so the U.S. Geological Survey (USGS) has developed a new water sampler, the MiniSipper, to provide long-duration, high-resolution water sampling in remote areas. The MiniSipper is a small, portable sampler that uses gas bubbles to separate up to 250 five milliliter acidified samples in a long tubing coil. The MiniSipper operates for over 8 months unattended in water under snow/ice, reduces field work costs, and greatly increases sampling resolution, especially during inaccessible times. MiniSippers were deployed in support of an U.S. Environmental Protection Agency (EPA) project evaluating acid mine drainage inputs from the Pennsylvania Mine to the Snake River watershed in Summit County, CO, USA. MiniSipper metal results agree within 10% of EPA-USGS hand collected grab sample results. Our high-resolution results reveal very strong correlations (R(2)>0.9) between potentially toxic metals (Cd, Cu, and Zn) and specific conductivity at the Pennsylvania Mine site. The large number of samples collected by the MiniSipper over the entire water year provides a detailed look at the effects of major hydrologic events such as snowmelt runoff and rainstorms on metal loading from the Pennsylvania Mine. MiniSipper results will help guide EPA sampling strategy and remediation efforts in the Snake River watershed. Published by Elsevier B.V.

Ground Truth Sampling and LANDSAT Accuracy Assessment

NASA Technical Reports Server (NTRS)

Robinson, J. W.; Gunther, F. J.; Campbell, W. J.

1982-01-01

It is noted that the key factor in any accuracy assessment of remote sensing data is the method used for determining the ground truth, independent of the remote sensing data itself. The sampling and accuracy procedures developed for nuclear power plant siting study are described. The purpose of the sampling procedure was to provide data for developing supervised classifications for two study sites and for assessing the accuracy of that and the other procedures used. The purpose of the accuracy assessment was to allow the comparison of the cost and accuracy of various classification procedures as applied to various data types.

Use of remote sensing for land use policy formulation. [Allegan, Bay, Branch, Ionia, Livingston, and St. Clair Counties in Michigan and Lake Michigan shorelines

NASA Technical Reports Server (NTRS)

Boylan, M. (Principal Investigator)

1978-01-01

The author has identified the following significant results. By utilizing remote sensing techniques, it was possible to accurately inventory a relatively large area for sand mining impact on protection and management of shoreland dunes within a limited time period and at a relatively low cost. Analysis of two sample areas selected from the Grand Mere area after prohibition of off-road-vehicle use indicated an increase in vegetation regrowth of 8.52% for sample area 1 and of 4.44% for sample area 2.

Remote Agent Demonstration

NASA Technical Reports Server (NTRS)

Dorais, Gregory A.; Kurien, James; Rajan, Kanna

1999-01-01

We describe the computer demonstration of the Remote Agent Experiment (RAX). The Remote Agent is a high-level, model-based, autonomous control agent being validated on the NASA Deep Space 1 spacecraft.

Forecasting long-range atmospheric transport episodes of polychlorinated biphenyls using FLEXPART

NASA Astrophysics Data System (ADS)

Halse, Anne Karine; Eckhardt, Sabine; Schlabach, Martin; Stohl, Andreas; Breivik, Knut

2013-06-01

The analysis of concentrations of persistent organic pollutants (POPs) in ambient air is costly and can only be done for a limited number of samples. It is thus beneficial to maximize the information content of the samples analyzed via a targeted observation strategy. Using polychlorinated biphenyls (PCBs) as an example, a forecasting system to predict and evaluate long-range atmospheric transport (LRAT) episodes of POPs at a remote site in southern Norway has been developed. The system uses the Lagrangian particle transport model FLEXPART, and can be used for triggering extra ("targeted") sampling when LRAT episodes are predicted to occur. The system was evaluated by comparing targeted samples collected over 12-25 h during individual LRAT episodes with monitoring samples regularly collected over one day per week throughout a year. Measured concentrations in all targeted samples were above the 75th percentile of the concentrations obtained from the regular monitoring program and included the highest measured values of all samples. This clearly demonstrates the success of the targeted sampling strategy.

Assessment of Various Remote Sensing Technologies in Biomass and Nitrogen Content Estimation Using AN Agricultural Test Field

NASA Astrophysics Data System (ADS)

Näsi, R.; Viljanen, N.; Kaivosoja, J.; Hakala, T.; Pandžić, M.; Markelin, L.; Honkavaara, E.

2017-10-01

Multispectral and hyperspectral imaging is usually acquired by satellite and aircraft platforms. Recently, miniaturized hyperspectral 2D frame cameras have showed great potential to precise agriculture estimations and they are feasible to combine with lightweight platforms, such as drones. Drone platform is a flexible tool for remote sensing applications with environment and agriculture. The assessment and comparison of different platforms such as satellite, aircraft and drones with different sensors, such as hyperspectral and RGB cameras is an important task in order to understand the potential of the data provided by these equipment and to select the most appropriate according to the user applications and requirements. In this context, open and permanent test fields are very significant and helpful experimental environment, since they provide a comparative data for different platforms, sensors and users, allowing multi-temporal analyses as well. Objective of this work was to investigate the feasibility of an open permanent test field in context of precision agriculture. Satellite (Sentinel-2), aircraft and drones with hyperspectral and RGB cameras were assessed in this study to estimate biomass, using linear regression models and in-situ samples. Spectral data and 3D information were used and compared in different combinations to investigate the quality of the models. The biomass estimation accuracies using linear regression models were better than 90 % for the drone based datasets. The results showed that the use of spectral and 3D features together improved the estimation model. However, estimation of nitrogen content was less accurate with the evaluated remote sensing sensors. The open and permanent test field showed to be suitable to provide an accurate and reliable reference data for the commercial users and farmers.

Remote ischemic preconditioning fails to reduce infarct size in the Zucker fatty rat model of type-2 diabetes: role of defective humoral communication.

PubMed

Wider, Joseph; Undyala, Vishnu V R; Whittaker, Peter; Woods, James; Chen, Xuequn; Przyklenk, Karin

2018-03-09

Remote ischemic preconditioning (RIPC), the phenomenon whereby brief ischemic episodes in distant tissues or organs render the heart resistant to infarction, has been exhaustively demonstrated in preclinical models. Moreover, emerging evidence suggests that exosomes play a requisite role in conveying the cardioprotective signal from remote tissue to the myocardium. However, in cohorts displaying clinically common comorbidities-in particular, type-2 diabetes-the infarct-sparing effect of RIPC may be confounded for as-yet unknown reasons. To investigate this issue, we used an integrated in vivo and in vitro approach to establish whether: (1) the efficacy of RIPC is maintained in the Zucker fatty rat model of type-2 diabetes, (2) the humoral transfer of cardioprotective triggers initiated by RIPC are transported via exosomes, and (3) diabetes is associated with alterations in exosome-mediated communication. We report that a standard RIPC stimulus (four 5-min episodes of hindlimb ischemia) reduced infarct size in normoglycemic Zucker lean rats, but failed to confer protection in diabetic Zucker fatty animals. Moreover, we provide novel evidence, via transfer of serum and serum fractions obtained following RIPC and applied to HL-1 cardiomyocytes subjected to hypoxia-reoxygenation, that diabetes was accompanied by impaired humoral communication of cardioprotective signals. Specifically, our data revealed that serum and exosome-rich serum fractions collected from normoglycemic rats attenuated hypoxia-reoxygenation-induced HL-1 cell death, while, in contrast, exosome-rich samples from Zucker fatty rats did not evoke protection in the HL-1 cell model. Finally, and unexpectedly, we found that exosome-depleted serum from Zucker fatty rats was cytotoxic and exacerbated hypoxia-reoxygenation-induced cardiomyocyte death.

Ambient aerodynamic ionization source for remote analyte sampling and mass spectrometric analysis.

PubMed

Dixon, R Brent; Sampson, Jason S; Hawkridge, Adam M; Muddiman, David C

2008-07-01

The use of aerodynamic devices in ambient ionization source development has become increasingly prevalent in the field of mass spectrometry. In this study, an air ejector has been constructed from inexpensive, commercially available components to incorporate an electrospray ionization emitter within the exhaust jet of the device. This novel aerodynamic device, herein termed remote analyte sampling, transport, and ionization relay (RASTIR) was used to remotely sample neutral species in the ambient and entrain them into an electrospray plume where they were subsequently ionized and detected using a linear ion trap Fourier transform mass spectrometer. Two sets of experiments were performed in the ambient environment to demonstrate the device's utility. The first involved the remote (approximately 1 ft) vacuum collection of pure sample particulates (i.e., dry powder) from a glass slide, entrainment and ionization at the ESI emitter, and mass spectrometric detection. The second experiment involved the capture (vacuum collection) of matrix-assisted laser desorbed proteins followed by entrainment in the ESI emitter plume, multiple charging, and mass spectrometric detection. This approach is in principle a RASTIR-assisted matrix-assisted laser desorption electrospray ionization source (Sampson, J. S.; Hawkridge, A. M.; Muddiman, D. C. J. Am. Soc. Mass Spectrom. 2006, 17, 1712-1716; Rapid Commun. Mass Spectrom. 2007, 21, 1150-1154.). A detailed description of the device construction, operational parameters, and preliminary small molecule and protein data are presented.

Lessons from UNSCOM and IAEA regarding remote monitoring and air sampling

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

Dupree, S.A.

1996-01-01

In 1991, at the direction of the United Nations Security Council, UNSCOM and IAEA developed plans for On-going Monitoring and Verification (OMV) in Iraq. The plans were accepted by the Security Council and remote monitoring and atmospheric sampling equipment has been installed at selected sites in Iraq. The remote monitoring equipment consists of video cameras and sensors positioned to observe equipment or activities at sites that could be used to support the development or manufacture of weapons of mass destruction, or long-range missiles. The atmospheric sampling equipment provides unattended collection of chemical samples from sites that could be used tomore » support the development or manufacture of chemical weapon agents. To support OMV in Iraq, UNSCOM has established the Baghdad Monitoring and Verification Centre. Imagery from the remote monitoring cameras can be accessed in near-real time from the Centre through RIF communication links with the monitored sites. The OMV program in Iraq has implications for international cooperative monitoring in both global and regional contexts. However, monitoring systems such as those used in Iraq are not sufficient, in and of themselves, to guarantee the absence of prohibited activities. Such systems cannot replace on-site inspections by competent, trained inspectors. However, monitoring similar to that used in Iraq can contribute to openness and confidence building, to the development of mutual trust, and to the improvement of regional stability.« less

Integrating remotely sensed surface water extent into continental scale hydrology

NASA Astrophysics Data System (ADS)

Revilla-Romero, Beatriz; Wanders, Niko; Burek, Peter; Salamon, Peter; de Roo, Ad

2016-12-01

In hydrological forecasting, data assimilation techniques are employed to improve estimates of initial conditions to update incorrect model states with observational data. However, the limited availability of continuous and up-to-date ground streamflow data is one of the main constraints for large-scale flood forecasting models. This is the first study that assess the impact of assimilating daily remotely sensed surface water extent at a 0.1° × 0.1° spatial resolution derived from the Global Flood Detection System (GFDS) into a global rainfall-runoff including large ungauged areas at the continental spatial scale in Africa and South America. Surface water extent is observed using a range of passive microwave remote sensors. The methodology uses the brightness temperature as water bodies have a lower emissivity. In a time series, the satellite signal is expected to vary with changes in water surface, and anomalies can be correlated with flood events. The Ensemble Kalman Filter (EnKF) is a Monte-Carlo implementation of data assimilation and used here by applying random sampling perturbations to the precipitation inputs to account for uncertainty obtaining ensemble streamflow simulations from the LISFLOOD model. Results of the updated streamflow simulation are compared to baseline simulations, without assimilation of the satellite-derived surface water extent. Validation is done in over 100 in situ river gauges using daily streamflow observations in the African and South American continent over a one year period. Some of the more commonly used metrics in hydrology were calculated: KGE', NSE, PBIAS%, R2, RMSE, and VE. Results show that, for example, NSE score improved on 61 out of 101 stations obtaining significant improvements in both the timing and volume of the flow peaks. Whereas the validation at gauges located in lowland jungle obtained poorest performance mainly due to the closed forest influence on the satellite signal retrieval. The conclusion is that remotely sensed surface water extent holds potential for improving rainfall-runoff streamflow simulations, potentially leading to a better forecast of the peak flow.

Remote Sensing of Soil Moisture: A Comparison of Optical and Thermal Methods

NASA Astrophysics Data System (ADS)

Foroughi, H.; Naseri, A. A.; Boroomandnasab, S.; Sadeghi, M.; Jones, S. B.; Tuller, M.; Babaeian, E.

2017-12-01

Recent technological advances in satellite and airborne remote sensing have provided new means for large-scale soil moisture monitoring. Traditional methods for soil moisture retrieval require thermal and optical RS observations. In this study we compared the traditional trapezoid model parameterized based on the land surface temperature - normalized difference vegetation index (LST-NDVI) space with the recently developed optical trapezoid model OPTRAM parameterized based on the shortwave infrared transformed reflectance (STR)-NDVI space for an extensive sugarcane field located in Southwestern Iran. Twelve Landsat-8 satellite images were acquired during the sugarcane growth season (April to October 2016). Reference in situ soil moisture data were obtained at 22 locations at different depths via core sampling and oven-drying. The obtained results indicate that the thermal/optical and optical prediction methods are comparable, both with volumetric moisture content estimation errors of about 0.04 cm3 cm-3. However, the OPTRAM model is more efficient because it does not require thermal data and can be universally parameterized for a specific location, because unlike the LST-soil moisture relationship, the reflectance-soil moisture relationship does not significantly vary with environmental variables (e.g., air temperature, wind speed, etc.).

The benefits of using remotely sensed soil moisture in parameter identification of large-scale hydrological models

NASA Astrophysics Data System (ADS)

Wanders, N.; Bierkens, M. F. P.; de Jong, S. M.; de Roo, A.; Karssenberg, D.

2014-08-01

Large-scale hydrological models are nowadays mostly calibrated using observed discharge. As a result, a large part of the hydrological system, in particular the unsaturated zone, remains uncalibrated. Soil moisture observations from satellites have the potential to fill this gap. Here we evaluate the added value of remotely sensed soil moisture in calibration of large-scale hydrological models by addressing two research questions: (1) Which parameters of hydrological models can be identified by calibration with remotely sensed soil moisture? (2) Does calibration with remotely sensed soil moisture lead to an improved calibration of hydrological models compared to calibration based only on discharge observations, such that this leads to improved simulations of soil moisture content and discharge? A dual state and parameter Ensemble Kalman Filter is used to calibrate the hydrological model LISFLOOD for the Upper Danube. Calibration is done using discharge and remotely sensed soil moisture acquired by AMSR-E, SMOS, and ASCAT. Calibration with discharge data improves the estimation of groundwater and routing parameters. Calibration with only remotely sensed soil moisture results in an accurate identification of parameters related to land-surface processes. For the Upper Danube upstream area up to 40,000 km2, calibration on both discharge and soil moisture results in a reduction by 10-30% in the RMSE for discharge simulations, compared to calibration on discharge alone. The conclusion is that remotely sensed soil moisture holds potential for calibration of hydrological models, leading to a better simulation of soil moisture content throughout the catchment and a better simulation of discharge in upstream areas. This article was corrected on 15 SEP 2014. See the end of the full text for details.

The review of dynamic monitoring technology for crop growth

NASA Astrophysics Data System (ADS)

Zhang, Hong-wei; Chen, Huai-liang; Zou, Chun-hui; Yu, Wei-dong

2010-10-01

In this paper, crop growth monitoring methods are described elaborately. The crop growth models, Netherlands-Wageningen model system, the United States-GOSSYM model and CERES models, Australia APSIM model and CCSODS model system in China, are introduced here more focus on the theories of mechanism, applications, etc. The methods and application of remote sensing monitoring methods, which based on leaf area index (LAI) and biomass were proposed by different scholars at home and abroad, are highly stressed in the paper. The monitoring methods of remote sensing coupling with crop growth models are talked out at large, including the method of "forced law" which using remote sensing retrieval state parameters as the crop growth model parameters input, and then to enhance the dynamic simulation accuracy of crop growth model and the method of "assimilation of Law" which by reducing the gap difference between the value of remote sensing retrieval and the simulated values of crop growth model and thus to estimate the initial value or parameter values to increasing the simulation accuracy. At last, the developing trend of monitoring methods are proposed based on the advantages and shortcomings in previous studies, it is assured that the combination of remote sensing with moderate resolution data of FY-3A, MODIS, etc., crop growth model, "3S" system and observation in situ are the main methods in refinement of dynamic monitoring and quantitative assessment techniques for crop growth in future.

Remote sensing validation through SOOP technology: implementation of Spectra system

NASA Astrophysics Data System (ADS)

Piermattei, Viviana; Madonia, Alice; Bonamano, Simone; Consalvi, Natalizia; Caligiore, Aurelio; Falcone, Daniela; Puri, Pio; Sarti, Fabio; Spaccavento, Giovanni; Lucarini, Diego; Pacci, Giacomo; Amitrano, Luigi; Iacullo, Salvatore; D'Andrea, Salvatore; Marcelli, Marco

2017-04-01

The development of low-cost instrumentation plays a key role in marine environmental studies and represents one of the most innovative aspects of marine research. The availability of low-cost technologies allows the realization of extended observatory networks for the study of marine phenomena through an integrated approach merging observations, remote sensing and operational oceanography. Marine services and practical applications critically depends on the availability of large amount of data collected with sufficiently dense spatial and temporal sampling. This issue directly influences the robustness both of ocean forecasting models and remote sensing observations through data assimilation and validation processes, particularly in the biological domain. For this reason it is necessary the development of cheap, small and integrated smart sensors, which could be functional both for satellite data validation and forecasting models data assimilation as well as to support early warning systems for environmental pollution control and prevention. This is particularly true in coastal areas, which are subjected to multiple anthropic pressures. Moreover, coastal waters can be classified like case 2 waters, where the optical properties of inorganic suspended matter and chromophoric dissolved organic matter must be considered and separated by the chlorophyll a contribution. Due to the high costs of mooring systems, research vessels, measure platforms and instrumentation a big effort was dedicated to the design, development and realization of a new low cost mini-FerryBox system: Spectra. Thanks to the modularity and user-friendly employment of the system, Spectra allows to acquire continuous in situ measures of temperature, conductivity, turbidity, chlorophyll a and chromophoric dissolved organic matter (CDOM) fluorescences from voluntary vessels, even by non specialized operators (Marcelli et al., 2014; 2016). This work shows the preliminary application of this technology to remote sensing data validation.

Study of Diagenetic Features in Rudist Buildups of Cretaceous Edwards Formation Using Ground Based Hyperspectral Scanning and Terrestrial LiDAR

NASA Astrophysics Data System (ADS)

Krupnik, D.; Khan, S.; Okyay, U.; Hartzell, P. J.; Biber, K.

2015-12-01

Ground based remote sensing is a novel technique for development of digital outcrop models which can be instrumental in performing detailed qualitative and quantitative sedimentological analysis for the study of depositional environment, diagenetic processes, and hydrocarbon reservoir characterization. For this investigation, ground-based hyperspectral data collection is combined with terrestrial LiDAR to study outcrops of Late Albian rudist buildups of the Edwards formation in the Lake Georgetown Spillway in Williamson County, Texas. The Edwards formation consists of shallow water deposits of reef and associated inter-reef facies, including rudist bioherms and biostromes. It is a significant aquifer and was investigated as a hydrocarbon play in south central Texas. Hyperspectral data were used to map compositional variation in the outcrop by distinguishing spectral properties unique to each material. Lithological variation was mapped in detail to investigate the structure and composition of rudist buildups. Hyperspectral imagery was registered to a 3D model produced from the LiDAR point cloud with an accuracy of up to one pixel. Flat-topped toucasid-rich bioherm facies were distinguished from overlying toucasid-rich biostrome facies containing chert nodules, overlying sucrosic dolostones, and uppermost peloid wackestones and packstones of back-reef facies. Ground truth was established by petrographic study of samples from this area and has validated classification products of remote sensing data. Several types of porosity were observed and have been associated with increased dolomitization. This ongoing research involves integration of remotely sensed datasets to analyze geometrical and compositional properties of this carbonate formation at a finer scale than traditional methods have achieved and seeks to develop a workflow for quick and efficient ground based remote sensing-assisted outcrop studies.

Gaseous Elemental Mercury (GEM) Emissions from Snow Surfaces in Northern New York

PubMed Central

Maxwell, J. Alexander; Holsen, Thomas M.; Mondal, Sumona

2013-01-01

Snow surface-to-air exchange of gaseous elemental mercury (GEM) was measured using a modified Teflon fluorinated ethylene propylene (FEP) dynamic flux chamber (DFC) in a remote, open site in Potsdam, New York. Sampling was conducted during the winter months of 2011. The inlet and outlet of the DFC were coupled with a Tekran Model 2537A mercury (Hg) vapor analyzer using a Tekran Model 1110 two port synchronized sampler. The surface GEM flux ranged from −4.47 ng m−2 hr−1 to 9.89 ng m−2 hr−1. For most sample periods, daytime GEM flux was strongly correlated with solar radiation. The average nighttime GEM flux was slightly negative and was not well correlated with any of the measured meteorological variables. Preliminary, empirical models were developed to estimate GEM emissions from snow surfaces in northern New York. These models suggest that most, if not all, of the Hg deposited with and to snow is reemitted to the atmosphere. PMID:23874951

Gaseous elemental mercury (GEM) emissions from snow surfaces in northern New York.

PubMed

Maxwell, J Alexander; Holsen, Thomas M; Mondal, Sumona

2013-01-01

Snow surface-to-air exchange of gaseous elemental mercury (GEM) was measured using a modified Teflon fluorinated ethylene propylene (FEP) dynamic flux chamber (DFC) in a remote, open site in Potsdam, New York. Sampling was conducted during the winter months of 2011. The inlet and outlet of the DFC were coupled with a Tekran Model 2537A mercury (Hg) vapor analyzer using a Tekran Model 1110 two port synchronized sampler. The surface GEM flux ranged from -4.47 ng m(-2) hr(-1) to 9.89 ng m(-2) hr(-1). For most sample periods, daytime GEM flux was strongly correlated with solar radiation. The average nighttime GEM flux was slightly negative and was not well correlated with any of the measured meteorological variables. Preliminary, empirical models were developed to estimate GEM emissions from snow surfaces in northern New York. These models suggest that most, if not all, of the Hg deposited with and to snow is reemitted to the atmosphere.

Portable open-path optical remote sensing (ORS) Fourier transform infrared (FTIR) instrumentation miniaturization and software for point and click real-time analysis

NASA Astrophysics Data System (ADS)

Zemek, Peter G.; Plowman, Steven V.

2010-04-01

Advances in hardware have miniaturized the emissions spectrometer and associated optics, rendering them easily deployed in the field. Such systems are also suitable for vehicle mounting, and can provide high quality data and concentration information in minutes. Advances in software have accompanied this hardware evolution, enabling the development of portable point-and-click OP-FTIR systems that weigh less than 16 lbs. These systems are ideal for first-responders, military, law enforcement, forensics, and screening applications using optical remote sensing (ORS) methodologies. With canned methods and interchangeable detectors, the new generation of OP-FTIR technology is coupled to the latest forward reference-type model software to provide point-and-click technology. These software models have been established for some time. However, refined user-friendly models that use active, passive, and solar occultation methodologies now allow the user to quickly field-screen and quantify plumes, fence-lines, and combustion incident scenarios in high-temporal-resolution. Synthetic background generation is now redundant as the models use highly accurate instrument line shape (ILS) convolutions and several other parameters, in conjunction with radiative transfer model databases to model a single calibration spectrum to collected sample spectra. Data retrievals are performed directly on single beam spectra using non-linear classical least squares (NLCLS). Typically, the Hitran line database is used to generate the initial calibration spectrum contained within the software.

Preliminary results of investigations into the use of artificial neural networks for discriminating gas chromatograph mass spectra of remote samples

NASA Technical Reports Server (NTRS)

Geller, Harold A.; Norris, Eugene; Warnock, Archibald, III

1991-01-01

Neural networks trained using mass spectra data from the National Institute of Standards and Technology (NIST) are studied. The investigations also included sample data from the gas chromatograph mass spectrometer (GCMS) instrument aboard the Viking Lander, obtained from the National Space Science Data Center. The work performed to data and the preliminary results from the training and testing of neural networks are described. These preliminary results are presented for the purpose of determining the viability of applying artificial neural networks in discriminating mass spectra samples from remote instrumentation such as the Mars Rover Sample Return Mission and the Cassini Probe.

Graphic products used in the evaluation of traditional and emerging remote sensing technologies for the detection of fugitive contamination at selected superfund hazardous waste sites

USGS Publications Warehouse

Slonecker, E. Terrence; Fisher, Gary B.

2011-01-01

This report presents the overhead imagery and field sampling results used to prepare U.S. Geological Survey Open-File Report 2011-1050, 'Evaluation of Traditional and Emerging Remote Sensing Technologies for the Detection of Fugitive Contamination at Selected Superfund Hazardous Waste Sites'. These graphic products were used in the evaluation of remote sensing technology in postclosure monitoring of hazardous waste sites and represent an ongoing research effort. Soil sampling results presented here were accomplished with field portable x-ray fluoresence (XRF) technology and are used as screening tools only representing the current conditions of metals and other contaminants at selected Superfund hazardous waste sites.

Microfluidic-Based Platform for Universal Sample Preparation and Biological Assays Automation for Life-Sciences Research and Remote Medical Applications

NASA Astrophysics Data System (ADS)

Brassard, D.; Clime, L.; Daoud, J.; Geissler, M.; Malic, L.; Charlebois, D.; Buckley, N.; Veres, T.

2018-02-01

An innovative centrifugal microfluidic universal platform for remote bio-analytical assays automation required in life-sciences research and medical applications, including purification and analysis from body fluids of cellular and circulating markers.

Incorporating GIS and remote sensing for census population disaggregation

NASA Astrophysics Data System (ADS)

Wu, Shuo-Sheng'derek'

Census data are the primary source of demographic data for a variety of researches and applications. For confidentiality issues and administrative purposes, census data are usually released to the public by aggregated areal units. In the United States, the smallest census unit is census blocks. Due to data aggregation, users of census data may have problems in visualizing population distribution within census blocks and estimating population counts for areas not coinciding with census block boundaries. The main purpose of this study is to develop methodology for estimating sub-block areal populations and assessing the estimation errors. The City of Austin, Texas was used as a case study area. Based on tax parcel boundaries and parcel attributes derived from ancillary GIS and remote sensing data, detailed urban land use classes were first classified using a per-field approach. After that, statistical models by land use classes were built to infer population density from other predictor variables, including four census demographic statistics (the Hispanic percentage, the married percentage, the unemployment rate, and per capita income) and three physical variables derived from remote sensing images and building footprints vector data (a landscape heterogeneity statistics, a building pattern statistics, and a building volume statistics). In addition to statistical models, deterministic models were proposed to directly infer populations from building volumes and three housing statistics, including the average space per housing unit, the housing unit occupancy rate, and the average household size. After population models were derived or proposed, how well the models predict populations for another set of sample blocks was assessed. The results show that deterministic models were more accurate than statistical models. Further, by simulating the base unit for modeling from aggregating blocks, I assessed how well the deterministic models estimate sub-unit-level populations. I also assessed the aggregation effects and the resealing effects on sub-unit estimates. Lastly, from another set of mixed-land-use sample blocks, a mixed-land-use model was derived and compared with a residential-land-use model. The results of per-field land use classification are satisfactory with a Kappa accuracy statistics of 0.747. Model Assessments by land use show that population estimates for multi-family land use areas have higher errors than those for single-family land use areas, and population estimates for mixed land use areas have higher errors than those for residential land use areas. The assessments of sub-unit estimates using a simulation approach indicate that smaller areas show higher estimation errors, estimation errors do not relate to the base unit size, and resealing improves all levels of sub-unit estimates.

Correlation Between Optoelectronic and Positron Lifetime Properties in As-received and Plasma-treated ZnO Nanopowders

NASA Astrophysics Data System (ADS)

Peters, R. M.; Paramo, J. A.; Quarles, C. A.; Strzhemechny, Y. M.

2009-03-01

We employed photoluminescence and positron lifetime measurements on a number of commercially available ZnO nanopowders. The experiments were performed before and after processing of these samples in remote N and O/He plasma. In all the nanopowders, the average lifetime component is substantially longer than in a single-crystalline sample, consistent with the model of grains with defect-rich surface and subsurface layers. However, the sample-to-sample differences in the quality of the powders, as detected by the photoluminescence spectroscopy, obscure observation of possible size effects. Compression of the powders into pellets yields reductions of the average positron lifetimes. Plasma-induced modifications are most visible in the low-temperature photoluminescence spectra of the smallest nanocrystals, indicative of a surface-specific nature of the chosen treatment procedure.

Statistical theory and methodology for remote sensing data analysis

NASA Technical Reports Server (NTRS)

Odell, P. L.

1974-01-01

A model is developed for the evaluation of acreages (proportions) of different crop-types over a geographical area using a classification approach and methods for estimating the crop acreages are given. In estimating the acreages of a specific croptype such as wheat, it is suggested to treat the problem as a two-crop problem: wheat vs. nonwheat, since this simplifies the estimation problem considerably. The error analysis and the sample size problem is investigated for the two-crop approach. Certain numerical results for sample sizes are given for a JSC-ERTS-1 data example on wheat identification performance in Hill County, Montana and Burke County, North Dakota. Lastly, for a large area crop acreages inventory a sampling scheme is suggested for acquiring sample data and the problem of crop acreage estimation and the error analysis is discussed.

Remote Sensing based modelling of Annual Surface Mass Balances of Chhota Shigiri Glacier, Western Himalayas, India

NASA Astrophysics Data System (ADS)

Chandrasekharan, Anita; Ramsankaran, Raaj

2017-04-01

The current study aims at modelling glacier mass balances over Chhota Shigiri glacier (32.28o N; 77.58° E) in Himachal Pradesh, India using the Equilibrium Line Altitude (ELA) gradient approach proposed by Rabatel et al. (2005). The model requires yearly ELA, average mass balance and mass balance gradient to estimate annual mass balance of a glacier which can be obtained either through field measurements or remote sensing observations. However, in view of the general scenario of lack of field data for Himalayan glaciers, in this study the model has been applied only using the inputs derived through multi-temporal satellite remote sensing observations thus eliminating the need for any field measurements. Preliminary analysis show that the obtained results are comparable with the observed field mass balance. The results also demonstrate that this approach with remote sensing inputs has potential to be used for glacier mass balance estimations provided good quality multi-temporal remote sensing dataset are available.

Remote electrochemical sensor

DOEpatents

Wang, Joseph; Olsen, Khris; Larson, David

1997-01-01

An electrochemical sensor for remote detection, particularly useful for metal contaminants and organic or other compounds. The sensor circumvents technical difficulties that previously prevented in-situ remote operations. The microelectrode, connected to a long communications cable, allows convenient measurements of the element or compound at timed and frequent intervals and instrument/sample distances of ten feet to more than 100 feet. The sensor is useful for both downhole groundwater monitoring and in-situ water (e.g., shipboard seawater) analysis.

[Measurement model of carbon emission from forest fire: a review].

PubMed

Hu, Hai-Qing; Wei, Shu-Jing; Jin, Sen; Sun, Long

2012-05-01

Forest fire is the main disturbance factor for forest ecosystem, and an important pathway of the decrease of vegetation- and soil carbon storage. Large amount of carbonaceous gases in forest fire can release into atmosphere, giving remarkable impacts on the atmospheric carbon balance and global climate change. To scientifically and effectively measure the carbonaceous gases emission from forest fire is of importance in understanding the significance of forest fire in the carbon balance and climate change. This paper reviewed the research progress in the measurement model of carbon emission from forest fire, which covered three critical issues, i. e., measurement methods of forest fire-induced total carbon emission and carbonaceous gases emission, affecting factors and measurement parameters of measurement model, and cause analysis of the uncertainty in the measurement of the carbon emissions. Three path selections to improve the quantitative measurement of the carbon emissions were proposed, i. e., using high resolution remote sensing data and improving algorithm and estimation accuracy of burned area in combining with effective fuel measurement model to improve the accuracy of the estimated fuel load, using high resolution remote sensing images combined with indoor controlled environment experiments, field measurements, and field ground surveys to determine the combustion efficiency, and combining indoor controlled environment experiments with field air sampling to determine the emission factors and emission ratio.

Remote sensing and urban public health

NASA Technical Reports Server (NTRS)

Rush, M.; Vernon, S.

1975-01-01

The applicability of remote sensing in the form of aerial photography to urban public health problems is examined. Environmental characteristics are analyzed to determine if health differences among areas could be predicted from the visual expression of remote sensing data. The analysis is carried out on a socioeconomic cross-sectional sample of census block groups. Six morbidity and mortality rates are the independent variables while environmental measures from aerial photographs and from the census constitute the two independent variable sets. It is found that environmental data collected by remote sensing are as good as census data in evaluating rates of health outcomes.

Prediction of health levels by remote sensing

NASA Technical Reports Server (NTRS)

Rush, M.; Vernon, S.

1975-01-01

Measures of the environment derived from remote sensing were compared to census population/housing measures in their ability to discriminate among health status areas in two urban communities. Three hypotheses were developed to explore the relationships between environmental and health data. Univariate and multiple step-wise linear regression analyses were performed on data from two sample areas in Houston and Galveston, Texas. Environmental data gathered by remote sensing were found to equal or surpass census data in predicting rates of health outcomes. Remote sensing offers the advantages of data collection for any chosen area or time interval, flexibilities not allowed by the decennial census.

Subsonic stability and control derivatives for an unpowered, remotely piloted 3/8-scale F-15 airplane model obtained from flight test

NASA Technical Reports Server (NTRS)

Iliff, K. W.; Maine, R. E.; Shafer, M. F.

1976-01-01

In response to the interest in airplane configuration characteristics at high angles of attack, an unpowered remotely piloted 3/8-scale F-15 airplane model was flight tested. The subsonic stability and control characteristics of this airplane model over an angle of attack range of -20 to 53 deg are documented. The remotely piloted technique for obtaining flight test data was found to provide adequate stability and control derivatives. The remotely piloted technique provided an opportunity to test the aircraft mathematical model in an angle of attack regime not previously examined in flight test. The variation of most of the derivative estimates with angle of attack was found to be consistent, particularly when the data were supplemented by uncertainty levels.

Remote Sensing Sensors and Applications in Environmental Resources Mapping and Modelling

PubMed Central

Melesse, Assefa M.; Weng, Qihao; S.Thenkabail, Prasad; Senay, Gabriel B.

2007-01-01

The history of remote sensing and development of different sensors for environmental and natural resources mapping and data acquisition is reviewed and reported. Application examples in urban studies, hydrological modeling such as land-cover and floodplain mapping, fractional vegetation cover and impervious surface area mapping, surface energy flux and micro-topography correlation studies is discussed. The review also discusses the use of remotely sensed-based rainfall and potential evapotranspiration for estimating crop water requirement satisfaction index and hence provides early warning information for growers. The review is not an exhaustive application of the remote sensing techniques rather a summary of some important applications in environmental studies and modeling. PMID:28903290

Practical lessons in remote connectivity.

PubMed Central

Kouroubali, A.; Starren, J.; Barrows, R. C.; Clayton, P. D.

1997-01-01

Community Health Information Networks (CHINs) require the ability to provide computer network connections to many remote sites. During the implementation of the Washington Heights and Inwood Community Health Management Information System (WHICHIS) at the Columbia-Presbyterian Medical Center (CPMC), a number of remote connectivity issues have been encountered. Both technical and non-technical issues were significant during the installation. We developed a work-flow model for this process which may be helpful to any health care institution attempting to provide seamless remote connectivity. This model is presented and implementation lessons are discussed. PMID:9357643

Remote sensing of land use and water quality relationships - Wisconsin shore, Lake Michigan

NASA Technical Reports Server (NTRS)

Haugen, R. K.; Marlar, T. L.

1976-01-01

This investigation assessed the utility of remote sensing techniques in the study of land use-water quality relationships in an east central Wisconsin test area. The following types of aerial imagery were evaluated: high altitude (60,000 ft) color, color infrared, multispectral black and white, and thermal; low altitude (less than 5000 ft) color infrared, multispectral black and white, thermal, and passive microwave. A non-imaging hand-held four-band radiometer was evaluated for utility in providing data on suspended sediment concentrations. Land use analysis includes the development of mapping and quantification methods to obtain baseline data for comparison to water quality variables. Suspended sediment loads in streams, determined from water samples, were related to land use differences and soil types in three major watersheds. A multiple correlation coefficient R of 0.85 was obtained for the relationship between the 0.6-0.7 micrometer incident and reflected radiation data from the hand-held radiometer and concurrent ground measurements of suspended solids in streams. Applications of the methods and baseline data developed in this investigation include: mapping and quantification of land use; input to watershed runoff models; estimation of effects of land use changes on stream sedimentation; and remote sensing of suspended sediment content of streams. High altitude color infrared imagery was found to be the most acceptable remote sensing technique for the mapping and measurement of land use types.

The study of active tectonic based on hyperspectral remote sensing

NASA Astrophysics Data System (ADS)

Cui, J.; Zhang, S.; Zhang, J.; Shen, X.; Ding, R.; Xu, S.

2017-12-01

As of the latest technical methods, hyperspectral remote sensing technology has been widely used in each brach of the geosciences. However, it is still a blank for using the hyperspectral remote sensing to study the active structrure. Hyperspectral remote sensing, with high spectral resolution, continuous spectrum, continuous spatial data, low cost, etc, has great potentialities in the areas of stratum division and fault identification. Blind fault identification in plains and invisible fault discrimination in loess strata are the two hot problems in the current active fault research. Thus, the study of active fault based on the hyperspectral technology has great theoretical significance and practical value. Magnetic susceptibility (MS) records could reflect the rhythm alteration of the formation. Previous study shown that MS has correlation with spectral feature. In this study, the Emaokou section, located to the northwest of the town of Huairen, in Shanxi Province, has been chosen for invisible fault study. We collected data from the Emaokou section, including spectral data, hyperspectral image, MS data. MS models based on spectral features were established and applied to the UHD185 image for MS mapping. The results shown that MS map corresponded well to the loess sequences. It can recognize the stratum which can not identity by naked eyes. Invisible fault has been found in this section, which is useful for paleoearthquake analysis. The faults act as the conduit for migration of terrestrial gases, the fault zones, especially the structurally weak zones such as inrtersections or bends of fault, may has different material composition. We take Xiadian fault for study. Several samples cross-fault were collected and these samples were measured by ASD Field Spec 3 spectrometer. Spectral classification method has been used for spectral analysis, we found that the spectrum of the fault zone have four special spectral region(550-580nm, 600-700nm, 700-800nm and 800-900nm), which different with the spectrum of the none-fault zone. It could help us welly located the fault zone. The located result correspond well to the physical prospecting method result. The above study shown that Hypersepctral remote sensing technology provide a new method for active study.

Estimating Mixed Broadleaves Forest Stand Volume Using Dsm Extracted from Digital Aerial Images

NASA Astrophysics Data System (ADS)

Sohrabi, H.

2012-07-01

In mixed old growth broadleaves of Hyrcanian forests, it is difficult to estimate stand volume at plot level by remotely sensed data while LiDar data is absent. In this paper, a new approach has been proposed and tested for estimating stand forest volume. The approach is based on this idea that forest volume can be estimated by variation of trees height at plots. In the other word, the more the height variation in plot, the more the stand volume would be expected. For testing this idea, 120 circular 0.1 ha sample plots with systematic random design has been collected in Tonekaon forest located in Hyrcanian zone. Digital surface model (DSM) measure the height values of the first surface on the ground including terrain features, trees, building etc, which provides a topographic model of the earth's surface. The DSMs have been extracted automatically from aerial UltraCamD images so that ground pixel size for extracted DSM varied from 1 to 10 m size by 1m span. DSMs were checked manually for probable errors. Corresponded to ground samples, standard deviation and range of DSM pixels have been calculated. For modeling, non-linear regression method was used. The results showed that standard deviation of plot pixels with 5 m resolution was the most appropriate data for modeling. Relative bias and RMSE of estimation was 5.8 and 49.8 percent, respectively. Comparing to other approaches for estimating stand volume based on passive remote sensing data in mixed broadleaves forests, these results are more encouraging. One big problem in this method occurs when trees canopy cover is totally closed. In this situation, the standard deviation of height is low while stand volume is high. In future studies, applying forest stratification could be studied.

Cooperative Autonomous Observation of Volcanic Environments with sUAS

NASA Astrophysics Data System (ADS)

Ravela, S.

2015-12-01

The Cooperative Autonomous Observing System Project (CAOS) at the MIT Earth Signals and Systems Group has developed methodology and systems for dynamically mapping coherent fluids such as plumes using small unmanned aircraft systems (sUAS). In the CAOS approach, two classes of sUAS, one remote the other in-situ, implement a dynamic data-driven mapping system by closing the loop between Modeling, Estimation, Sampling, Planning and Control (MESPAC). The continually gathered measurements are assimilated to produce maps/analyses which also guide the sUAS network to adaptively resample the environment. Rather than scan the volume in fixed Eulerian or Lagrangian flight plans, the adaptive nature of the sampling process enables objectives for efficiency and resilience to be incorporated. Modeling includes realtime prediction using two types of reduced models, one based on nowcasting remote observations of plume tracer using scale-cascaded alignment, and another based on dynamically-deformable EOF/POD developed for coherent structures. Ensemble-based Information-theoretic machine learning approaches are used for the highly non-linear/non-Gaussian state/parameter estimation, and for planning. Control of the sUAS is based on model reference control coupled with hierarchical PID. MESPAC is implemented in part on a SkyCandy platform, and implements an airborne mesh that provides instantaneous situational awareness and redundant communication to an operating fleet. SkyCandy is deployed on Itzamna Aero's I9X/W UAS with low-cost sensors, and is currently being used to study the Popocatepetl volcano. Results suggest that operational communities can deploy low-cost sUAS to systematically monitor whilst optimizing for efficiency/maximizing resilience. The CAOS methodology is applicable to many other environments where coherent structures are present in the background. More information can be found at caos.mit.edu.

Improving three-tier environmental assessment model by using a 3D scanning FLS-AM series hyperspectral lidar

NASA Astrophysics Data System (ADS)

Samberg, Andre; Babichenko, Sergei; Poryvkina, Larisa

2005-05-01

Delay between the time when natural disaster, for example, oil accident in coastal water, occurred and the time when environmental protection actions, for example, water and shoreline clean-up, started is of significant importance. Mostly remote sensing techniques are considered as (near) real-time and suitable for multiple tasks. These techniques in combination with rapid environmental assessment methodologies would form multi-tier environmental assessment model, which allows creating (near) real-time datasets and optimizing sampling scenarios. This paper presents the idea of three-tier environmental assessment model. Here all three tiers are briefly described to show the linkages between them, with a particular focus on the first tier. Furthermore, it is described how large-scale environmental assessment can be improved by using an airborne 3-D scanning FLS-AM series hyperspectral lidar. This new aircraft-based sensor is typically applied for oil mapping on sea/ground surface and extracting optical features of subjects. In general, a sampling network, which is based on three-tier environmental assessment model, can include ship(s) and aircraft(s). The airborne 3-D scanning FLS-AM series hyperspectral lidar helps to speed up the whole process of assessing of area of natural disaster significantly, because this is a real-time remote sensing mean. For instance, it can deliver such information as georeferenced oil spill position in WGS-84, the estimated size of the whole oil spill, and the estimated amount of oil in seawater or on ground. All information is produced in digital form and, thus, can be directly transferred into a customer"s GIS (Geographical Information System) system.

BROADBAND DIGITAL GEOPHYSICAL TELEMETRY SYSTEM.

USGS Publications Warehouse

Seeley, Robert L.; Daniels, Jeffrey J.

1984-01-01

A system has been developed to simultaneously sample and transmit digital data from five remote geophysical data receiver stations to a control station that processes, displays, and stores the data. A microprocessor in each remote station receives commands from the control station over a single telemetry channel.

U.S. EPA High-Field NMR Facility with Remote Accessibility

EPA Science Inventory

EPA’s High-Field Nuclear Magnetic Resonance Research Facility housed in Athens, GA has two Varian 600 MHz NMR spectrometers used for conducting sophisticated experiments in environmental science. Off-site users can ship their samples and perform their NMR experiments remotely fr...

Application of Hyperspectral Remote Sensing Techniques to Evaluate Water Quality in Turbid Coastal Waters of South Carolina.

NASA Astrophysics Data System (ADS)

Ali, K. A.; Ryan, K.

2014-12-01

Coastal and inland waters represent a diverse set of resources that support natural habitat and provide valuable ecosystem services to the human population. Conventional techniques to monitor water quality using in situ sensors and laboratory analysis of water samples can be very time- and cost-intensive. Alternatively, remote sensing techniques offer better spatial coverage and temporal resolution to accurately characterize the dynamic and unique water quality parameters. Existing remote sensing ocean color products, such as the water quality proxy chlorophyll-a, are based on ocean derived bio-optical models that are primarily calibrated in Case 1 type waters. These traditional models fail to work when applied in turbid (Case 2 type), coastal waters due to spectral interference from other associated color producing agents such as colored dissolved organic matter and suspended sediments. In this work, we introduce a novel technique for the predictive modeling of chlorophyll-a using a multivariate-based approach applied to in situ hyperspectral radiometric data collected from the coastal waters of Long Bay, South Carolina. This method uses a partial least-squares regression model to identify prominent wavelengths that are more sensitive to chlorophyll-a relative to other associated color-producing agents. The new model was able to explain 80% of the observed chlorophyll-a variability in Long Bay with RMSE = 2.03 μg/L. This approach capitalizes on the spectral advantage gained from current and future hyperspectral sensors, thus providing a more robust predicting model. This enhanced mode of water quality monitoring in marine environments will provide insight to point-sources and problem areas that may contribute to a decline in water quality. The utility of this tool is in its versatility to a diverse set of coastal waters and its use by coastal and fisheries managers with regard to recreation, regulation, economic and public health purposes.

Assessing biomass of diverse coastal marsh ecosystems using statistical and machine learning models

NASA Astrophysics Data System (ADS)

Mo, Yu; Kearney, Michael S.; Riter, J. C. Alexis; Zhao, Feng; Tilley, David R.

2018-06-01

The importance and vulnerability of coastal marshes necessitate effective ways to closely monitor them. Optical remote sensing is a powerful tool for this task, yet its application to diverse coastal marsh ecosystems consisting of different marsh types is limited. This study samples spectral and biophysical data from freshwater, intermediate, brackish, and saline marshes in Louisiana, and develops statistical and machine learning models to assess the marshes' biomass with combined ground, airborne, and spaceborne remote sensing data. It is found that linear models derived from NDVI and EVI are most favorable for assessing Leaf Area Index (LAI) using multispectral data (R2 = 0.7 and 0.67, respectively), and the random forest models are most useful in retrieving LAI and Aboveground Green Biomass (AGB) using hyperspectral data (R2 = 0.91 and 0.84, respectively). It is also found that marsh type and plant species significantly impact the linear model development (P < .05 in both cases). Sensors with coarser spatial resolution yield lower LAI values because the fine water networks are not detected and mixed into the vegetation pixels. The Landsat OLI-derived map shows the LAI of coastal mashes in Louisiana mostly ranges from 0 to 5.0, and is highest for freshwater marshes and for marshes in the Atchafalaya Bay delta. The CASI-derived maps show that LAI of saline marshes at Bay Batiste typically ranges from 0.9 to 1.5, and the AGB is mostly less than 900 g/m2. This study provides solutions for assessing the biomass of Louisiana's coastal marshes using various optical remote sensing techniques, and highlights the impacts of the marshes' species composition on the model development and the sensors' spatial resolution on biomass mapping, thereby providing useful tools for monitoring the biomass of coastal marshes in Louisiana and diverse coastal marsh ecosystems elsewhere.

Quantitative extraction of the bedrock exposure rate based on unmanned aerial vehicle data and Landsat-8 OLI image in a karst environment

NASA Astrophysics Data System (ADS)

Wang, Hongyan; Li, Qiangzi; Du, Xin; Zhao, Longcai

2017-12-01

In the karst regions of southwest China, rocky desertification is one of the most serious problems in land degradation. The bedrock exposure rate is an important index to assess the degree of rocky desertification in karst regions. Because of the inherent merits of macro-scale, frequency, efficiency, and synthesis, remote sensing is a promising method to monitor and assess karst rocky desertification on a large scale. However, actual measurement of the bedrock exposure rate is difficult and existing remote-sensing methods cannot directly be exploited to extract the bedrock exposure rate owing to the high complexity and heterogeneity of karst environments. Therefore, using unmanned aerial vehicle (UAV) and Landsat-8 Operational Land Imager (OLI) data for Xingren County, Guizhou Province, quantitative extraction of the bedrock exposure rate based on multi-scale remote-sensing data was developed. Firstly, we used an object-oriented method to carry out accurate classification of UAVimages. From the results of rock extraction, the bedrock exposure rate was calculated at the 30 m grid scale. Parts of the calculated samples were used as training data; other data were used for model validation. Secondly, in each grid the band reflectivity of Landsat-8 OLI data was extracted and a variety of rock and vegetation indexes (e.g., NDVI and SAVI) were calculated. Finally, a network model was established to extract the bedrock exposure rate. The correlation coefficient of the network model was 0.855, that of the validation model was 0.677 and the root mean square error of the validation model was 0.073. This method is valuable for wide-scale estimation of bedrock exposure rate in karst environments. Using the quantitative inversion model, a distribution map of the bedrock exposure rate in Xingren County was obtained.

Fault detection and diagnosis for gas turbines based on a kernelized information entropy model.

PubMed

Wang, Weiying; Xu, Zhiqiang; Tang, Rui; Li, Shuying; Wu, Wei

2014-01-01

Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms.

A multi-year data set on aerosol-cloud-precipitation-meteorology interactions for marine stratocumulus clouds.

PubMed

Sorooshian, Armin; MacDonald, Alexander B; Dadashazar, Hossein; Bates, Kelvin H; Coggon, Matthew M; Craven, Jill S; Crosbie, Ewan; Hersey, Scott P; Hodas, Natasha; Lin, Jack J; Negrón Marty, Arnaldo; Maudlin, Lindsay C; Metcalf, Andrew R; Murphy, Shane M; Padró, Luz T; Prabhakar, Gouri; Rissman, Tracey A; Shingler, Taylor; Varutbangkul, Varuntida; Wang, Zhen; Woods, Roy K; Chuang, Patrick Y; Nenes, Athanasios; Jonsson, Haflidi H; Flagan, Richard C; Seinfeld, John H

2018-02-27

Airborne measurements of meteorological, aerosol, and stratocumulus cloud properties have been harmonized from six field campaigns during July-August months between 2005 and 2016 off the California coast. A consistent set of core instruments was deployed on the Center for Interdisciplinary Remotely-Piloted Aircraft Studies Twin Otter for 113 flight days, amounting to 514 flight hours. A unique aspect of the compiled data set is detailed measurements of aerosol microphysical properties (size distribution, composition, bioaerosol detection, hygroscopicity, optical), cloud water composition, and different sampling inlets to distinguish between clear air aerosol, interstitial in-cloud aerosol, and droplet residual particles in cloud. Measurements and data analysis follow documented methods for quality assurance. The data set is suitable for studies associated with aerosol-cloud-precipitation-meteorology-radiation interactions, especially owing to sharp aerosol perturbations from ship traffic and biomass burning. The data set can be used for model initialization and synergistic application with meteorological models and remote sensing data to improve understanding of the very interactions that comprise the largest uncertainty in the effect of anthropogenic emissions on radiative forcing.

Yosemite Hydroclimate Network: Distributed stream and atmospheric data for the Tuolumne River watershed and surroundings

NASA Astrophysics Data System (ADS)

Lundquist, Jessica D.; Roche, James W.; Forrester, Harrison; Moore, Courtney; Keenan, Eric; Perry, Gwyneth; Cristea, Nicoleta; Henn, Brian; Lapo, Karl; McGurk, Bruce; Cayan, Daniel R.; Dettinger, Michael D.

2016-09-01

Regions of complex topography and remote wilderness terrain have spatially varying patterns of temperature and streamflow, but due to inherent difficulties of access, are often very poorly sampled. Here we present a data set of distributed stream stage, streamflow, stream temperature, barometric pressure, and air temperature from the Tuolumne River Watershed in Yosemite National Park, Sierra Nevada, California, USA, for water years 2002-2015, as well as a quality-controlled hourly meteorological forcing time series for use in hydrologic modeling. We also provide snow data and daily inflow to the Hetch Hetchy Reservoir for 1970-2015. This paper describes data collected using low-visibility and low-impact installations for wilderness locations and can be used alone or as a critical supplement to ancillary data sets collected by cooperating agencies, referenced herein. This data set provides a unique opportunity to understand spatial patterns and scaling of hydroclimatic processes in complex terrain and can be used to evaluate downscaling techniques or distributed modeling. The paper also provides an example methodology and lessons learned in conducting hydroclimatic monitoring in remote wilderness.

A multi-year data set on aerosol-cloud-precipitation-meteorology interactions for marine stratocumulus clouds

PubMed Central

Sorooshian, Armin; MacDonald, Alexander B.; Dadashazar, Hossein; Bates, Kelvin H.; Coggon, Matthew M.; Craven, Jill S.; Crosbie, Ewan; Hersey, Scott P.; Hodas, Natasha; Lin, Jack J.; Negrón Marty, Arnaldo; Maudlin, Lindsay C.; Metcalf, Andrew R.; Murphy, Shane M.; Padró, Luz T.; Prabhakar, Gouri; Rissman, Tracey A.; Shingler, Taylor; Varutbangkul, Varuntida; Wang, Zhen; Woods, Roy K.; Chuang, Patrick Y.; Nenes, Athanasios; Jonsson, Haflidi H.; Flagan, Richard C.; Seinfeld, John H.

2018-01-01

Airborne measurements of meteorological, aerosol, and stratocumulus cloud properties have been harmonized from six field campaigns during July-August months between 2005 and 2016 off the California coast. A consistent set of core instruments was deployed on the Center for Interdisciplinary Remotely-Piloted Aircraft Studies Twin Otter for 113 flight days, amounting to 514 flight hours. A unique aspect of the compiled data set is detailed measurements of aerosol microphysical properties (size distribution, composition, bioaerosol detection, hygroscopicity, optical), cloud water composition, and different sampling inlets to distinguish between clear air aerosol, interstitial in-cloud aerosol, and droplet residual particles in cloud. Measurements and data analysis follow documented methods for quality assurance. The data set is suitable for studies associated with aerosol-cloud-precipitation-meteorology-radiation interactions, especially owing to sharp aerosol perturbations from ship traffic and biomass burning. The data set can be used for model initialization and synergistic application with meteorological models and remote sensing data to improve understanding of the very interactions that comprise the largest uncertainty in the effect of anthropogenic emissions on radiative forcing. PMID:29485627

Yosemite Hydroclimate Network: Distributed stream and atmospheric data for the Tuolumne River watershed and surroundings

USGS Publications Warehouse

Lundquist, Jessica D.; Roche, James W.; Forrester, Harrison; Moore, Courtney; Keenan, Eric; Perry, Gwyneth; Cristea, Nicoleta; Henn, Brian; Lapo, Karl; McGurk, Bruce; Cayan, Daniel R.; Dettinger, Michael D.

2016-01-01

Regions of complex topography and remote wilderness terrain have spatially varying patterns of temperature and streamflow, but due to inherent difficulties of access, are often very poorly sampled. Here we present a data set of distributed stream stage, streamflow, stream temperature, barometric pressure, and air temperature from the Tuolumne River Watershed in Yosemite National Park, Sierra Nevada, California, USA, for water years 2002–2015, as well as a quality-controlled hourly meteorological forcing time series for use in hydrologic modeling. We also provide snow data and daily inflow to the Hetch Hetchy Reservoir for 1970–2015. This paper describes data collected using low-visibility and low-impact installations for wilderness locations and can be used alone or as a critical supplement to ancillary data sets collected by cooperating agencies, referenced herein. This data set provides a unique opportunity to understand spatial patterns and scaling of hydroclimatic processes in complex terrain and can be used to evaluate downscaling techniques or distributed modeling. The paper also provides an example methodology and lessons learned in conducting hydroclimatic monitoring in remote wilderness.

A multi-year data set on aerosol-cloud-precipitation-meteorology interactions for marine stratocumulus clouds

NASA Astrophysics Data System (ADS)

Sorooshian, Armin; MacDonald, Alexander B.; Dadashazar, Hossein; Bates, Kelvin H.; Coggon, Matthew M.; Craven, Jill S.; Crosbie, Ewan; Hersey, Scott P.; Hodas, Natasha; Lin, Jack J.; Negrón Marty, Arnaldo; Maudlin, Lindsay C.; Metcalf, Andrew R.; Murphy, Shane M.; Padró, Luz T.; Prabhakar, Gouri; Rissman, Tracey A.; Shingler, Taylor; Varutbangkul, Varuntida; Wang, Zhen; Woods, Roy K.; Chuang, Patrick Y.; Nenes, Athanasios; Jonsson, Haflidi H.; Flagan, Richard C.; Seinfeld, John H.

2018-02-01

Airborne measurements of meteorological, aerosol, and stratocumulus cloud properties have been harmonized from six field campaigns during July-August months between 2005 and 2016 off the California coast. A consistent set of core instruments was deployed on the Center for Interdisciplinary Remotely-Piloted Aircraft Studies Twin Otter for 113 flight days, amounting to 514 flight hours. A unique aspect of the compiled data set is detailed measurements of aerosol microphysical properties (size distribution, composition, bioaerosol detection, hygroscopicity, optical), cloud water composition, and different sampling inlets to distinguish between clear air aerosol, interstitial in-cloud aerosol, and droplet residual particles in cloud. Measurements and data analysis follow documented methods for quality assurance. The data set is suitable for studies associated with aerosol-cloud-precipitation-meteorology-radiation interactions, especially owing to sharp aerosol perturbations from ship traffic and biomass burning. The data set can be used for model initialization and synergistic application with meteorological models and remote sensing data to improve understanding of the very interactions that comprise the largest uncertainty in the effect of anthropogenic emissions on radiative forcing.

Fault Detection and Diagnosis for Gas Turbines Based on a Kernelized Information Entropy Model

PubMed Central

Wang, Weiying; Xu, Zhiqiang; Tang, Rui; Li, Shuying; Wu, Wei

2014-01-01

Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms. PMID:25258726

Estimating crop biophysical properties from remote sensing data by inverting linked radiative transfer and ecophysiological models

USDA-ARS?s Scientific Manuscript database

Remote sensing technology can rapidly provide spatial information on crop growth status, which ideally could be used to invert radiative transfer models or ecophysiological models for estimating a variety of crop biophysical properties. However, the outcome of the model inversion procedure will be ...

Exploring Remote Rensing Through The Use Of Readily-Available Classroom Technologies

NASA Astrophysics Data System (ADS)

Rogers, M. A.

2013-12-01

Frontier geoscience research using remotely-sensed satellite observation routinely requires sophisticated and novel remote sensing techniques to succeed. Describing these techniques in an educational format presents significant challenges to the science educator, especially with regards to the professional development setting where a small, but competent audience has limited instructor contact time to develop the necessary understanding. In this presentation, we describe the use of simple and cheaply available technologies, including ultrasonic transducers, FLIR detectors, and even simple web cameras to provide a tangible analogue to sophisticated remote sensing platforms. We also describe methods of curriculum development that leverages the use of these simple devices to teach the fundamentals of remote sensing, resulting in a deeper and more intuitive understanding of the techniques used in modern remote sensing research. Sample workshop itineraries using these techniques are provided as well.

Remote sensing of wetlands

NASA Technical Reports Server (NTRS)

Roller, N. E. G.

1977-01-01

The concept of using remote sensing to inventory wetlands and the related topics of proper inventory design and data collection are discussed. The material presented shows that aerial photography is the form of remote sensing from which the greatest amount of wetlands information can be derived. For extensive, general-purpose wetlands inventories, however, the use of LANDSAT data may be more cost-effective. Airborne multispectral scanners and radar are, in the main, too expensive to use - unless the information that these sensors alone can gather remotely is absolutely required. Multistage sampling employing space and high altitude remote sensing data in the initial stages appears to be an efficient survey strategy for gathering non-point specific wetlands inventory data over large areas. The operational role of remote sensing insupplying inventory data for application to several typical wetlands management problems is illustrated by summary descriptions of past ERIM projects.

Applying narrowband remote-sensing reflectance models to wideband data.

PubMed

Lee, Zhongping

2009-06-10

Remote sensing of coastal and inland waters requires sensors to have a high spatial resolution to cover the spatial variation of biogeochemical properties in fine scales. High spatial-resolution sensors, however, are usually equipped with spectral bands that are wide in bandwidth (50 nm or wider). In this study, based on numerical simulations of hyperspectral remote-sensing reflectance of optically-deep waters, and using Landsat band specifics as an example, the impact of a wide spectral channel on remote sensing is analyzed. It is found that simple adoption of a narrowband model may result in >20% underestimation in calculated remote-sensing reflectance, and inversely may result in >20% overestimation in inverted absorption coefficients even under perfect conditions, although smaller (approximately 5%) uncertainties are found for higher absorbing waters. These results provide a cautious note, but also a justification for turbid coastal waters, on applying narrowband models to wideband data.

Viking Landers and remote sensing

NASA Technical Reports Server (NTRS)

Moore, H. J.; Jakosky, B. M.; Christensen, P. R.

1987-01-01

Thermal and radar remote sensing signatures of the materials in the lander sample fields can be crudely estimated from evaluations of their physical-mechanical properties, laboratory data on thermal conductivities and dielectric constants, and theory. The estimated thermal inertias and dielectric constants of some of the materials in the sample field are close to modal values estimated from orbital and earth-based observations. This suggests that the mechanical properties of the surface materials of much of Mars will not be significantly different that those of the landing sites.

First year of practical experiences of the new Arctic AWIPEV-COSYNA cabled Underwater Observatory in Kongsfjorden, Spitsbergen

NASA Astrophysics Data System (ADS)

Fischer, Philipp; Schwanitz, Max; Loth, Reiner; Posner, Uwe; Brand, Markus; Schröder, Friedhelm

2017-04-01

A combined year-round assessment of selected oceanographic data and a macrobiotic community assessment was performed from October 2013 to November 2014 in the littoral zone of the Kongsfjorden polar fjord system on the western coast of Svalbard (Norway). State of the art remote controlled cabled underwater observatory technology was used for daily vertical profiles of temperature, salinity, and turbidity together with a stereo-optical assessment of the macrobiotic community, including fish. The results reveal a distinct seasonal cycle in total species abundances, with a significantly higher total abundance and species richness during the polar winter when no light is available underwater compared to the summer months when 24 h light is available. During the winter months, a temporally highly segmented community was observed with respect to species occurrence, with single species dominating the winter community for restricted times. In contrast, the summer community showed an overall lower total abundance as well as a significantly lower number of species. The study clearly demonstrates the high potential of cable connected remote controlled digital sampling devices, especially in remote areas, such as polar fjord systems, with harsh environmental conditions and limited accessibility. A smart combination of such new digital sampling methods with classic sampling procedures can provide a possibility to significantly extend the sampling time and frequency, especially in remote and difficult to access areas. This can help to provide a sufficient data density and therefore statistical power for a sound scientific analysis without increasing the invasive sampling pressure in ecologically sensitive environments.

Airborne Lidar-Based Estimates of Tropical Forest Structure in Complex Terrain: Opportunities and Trade-Offs for REDD+

NASA Technical Reports Server (NTRS)

Leitold, Veronika; Keller, Michael; Morton, Douglas C.; Cook, Bruce D.; Shimabukuro, Yosio E.

2015-01-01

Background: Carbon stocks and fluxes in tropical forests remain large sources of uncertainty in the global carbon budget. Airborne lidar remote sensing is a powerful tool for estimating aboveground biomass, provided that lidar measurements penetrate dense forest vegetation to generate accurate estimates of surface topography and canopy heights. Tropical forest areas with complex topography present a challenge for lidar remote sensing. Results: We compared digital terrain models (DTM) derived from airborne lidar data from a mountainous region of the Atlantic Forest in Brazil to 35 ground control points measured with survey grade GNSS receivers. The terrain model generated from full-density (approx. 20 returns/sq m) data was highly accurate (mean signed error of 0.19 +/-0.97 m), while those derived from reduced-density datasets (8/sq m, 4/sq m, 2/sq m and 1/sq m) were increasingly less accurate. Canopy heights calculated from reduced-density lidar data declined as data density decreased due to the inability to accurately model the terrain surface. For lidar return densities below 4/sq m, the bias in height estimates translated into errors of 80-125 Mg/ha in predicted aboveground biomass. Conclusions: Given the growing emphasis on the use of airborne lidar for forest management, carbon monitoring, and conservation efforts, the results of this study highlight the importance of careful survey planning and consistent sampling for accurate quantification of aboveground biomass stocks and dynamics. Approaches that rely primarily on canopy height to estimate aboveground biomass are sensitive to DTM errors from variability in lidar sampling density.

Airborne lidar-based estimates of tropical forest structure in complex terrain: opportunities and trade-offs for REDD+

PubMed

Leitold, Veronika; Keller, Michael; Morton, Douglas C; Cook, Bruce D; Shimabukuro, Yosio E

2015-12-01

Carbon stocks and fluxes in tropical forests remain large sources of uncertainty in the global carbon budget. Airborne lidar remote sensing is a powerful tool for estimating aboveground biomass, provided that lidar measurements penetrate dense forest vegetation to generate accurate estimates of surface topography and canopy heights. Tropical forest areas with complex topography present a challenge for lidar remote sensing. We compared digital terrain models (DTM) derived from airborne lidar data from a mountainous region of the Atlantic Forest in Brazil to 35 ground control points measured with survey grade GNSS receivers. The terrain model generated from full-density (~20 returns m -2 ) data was highly accurate (mean signed error of 0.19 ± 0.97 m), while those derived from reduced-density datasets (8 m -2 , 4 m -2 , 2 m -2 and 1 m -2 ) were increasingly less accurate. Canopy heights calculated from reduced-density lidar data declined as data density decreased due to the inability to accurately model the terrain surface. For lidar return densities below 4 m -2 , the bias in height estimates translated into errors of 80-125 Mg ha -1 in predicted aboveground biomass. Given the growing emphasis on the use of airborne lidar for forest management, carbon monitoring, and conservation efforts, the results of this study highlight the importance of careful survey planning and consistent sampling for accurate quantification of aboveground biomass stocks and dynamics. Approaches that rely primarily on canopy height to estimate aboveground biomass are sensitive to DTM errors from variability in lidar sampling density.

From Ions to Bits - Developing the IT infrastructure around the CAMECA IMS 1280-HR SIMS lab at GFZ Potsdam

NASA Astrophysics Data System (ADS)

Galkin, A.; Klump, J.; Wiedenbeck, M.

2012-04-01

Secondary Ion Mass Spectrometers (SIMS) is an highly sensitive technique for analyzing the surfaces of solids and thin film samples, but has the major drawback that such instruments are both rare and expensive. The Virtual SIMS project aims to design, develop and operate the IT infrastructure around the CAMECA IMS 1280-HR SIMS at GFZ Potsdam. The system will cover the whole spectrum of the procedures in the lab - from the online application for measurement time, to the remote access to the instrument and finally the maintenance of the data for publishing and future re-use. A virtual lab infrastructure around the IMS 1280 will enable remote access to the instrument and make measurement time available to the broadest possible user community. Envisioned is that the IT infrastructure would consist of the following: web portal, data repository, sample repository, project management software, communication arrangements between the lab staff and distant researcher and remote access to the instruments. The web portal will handle online applications for the measurement time. The data from the experiments, the monitoring sensor logs and the lab logbook entries are to be stored and archived. Researchers will be able to access their data remotely in real time, thus imposing a user rights management strucuture. Also planned is that all samples and the standards will be assigned a unique International GeoSample Number (IGSN) and that the images of the samples will be stored and made accessible in addition to any additional documents which might be uploaded by the researcher. The project management application will schedule the application process, the measurements times, notifications and alerts. A video conference capability is forseen for communication between the Potsdam staff and the remote researcher. The remote access to the instruments requires a sophisticated client-server solution. This highly sensitive instrument has to be controlled in real-time with latencies diminished to a minimum. Also, failures and shortages of the internet connection, as well as possible outages on the client side, have to be considered and safe fallbacks for such events must be provided. The level of skills of the researcher remotely operating the instrument will define the scope of control given during an operating session. An important aspect of the project is the design of the virtual lab system in collaboration with the laboratory operators and the researchers who will use the instrument and its peripherals. Different approaches for the IT solutions will be tested and evaluated, so imporved guidelines can evolve from obsperved operating performance.

Towards Making Data Bases Practical for use in the Field

NASA Astrophysics Data System (ADS)

Fischer, T. P.; Lehnert, K. A.; Chiodini, G.; McCormick, B.; Cardellini, C.; Clor, L. E.; Cottrell, E.

2014-12-01

Geological, geochemical, and geophysical research is often field based with travel to remote areas and collection of samples and data under challenging environmental conditions. Cross-disciplinary investigations would greatly benefit from near real-time data access and visualisation within the existing framework of databases and GIS tools. An example of complex, interdisciplinary field-based and data intensive investigations is that of volcanologists and gas geochemists, who sample gases from fumaroles, hot springs, dry gas vents, hydrothermal vents and wells. Compositions of volcanic gas plumes are measured directly or by remote sensing. Soil gas fluxes from volcanic areas are measured by accumulation chamber and involve hundreds of measurements to calculate the total emission of a region. Many investigators also collect rock samples from recent or ancient volcanic eruptions. Structural, geochronological, and geophysical data collected during the same or related field campaigns complement these emissions data. All samples and data collected in the field require a set of metadata including date, time, location, sample or measurement id, and descriptive comments. Currently, most of these metadata are written in field notebooks and later transferred into a digital format. Final results such as laboratory analyses of samples and calculated flux data are tabulated for plotting, correlation with other types of data, modeling and finally publication and presentation. Data handling, organization and interpretation could be greatly streamlined by using digital tools available in the field to record metadata, assign an International Geo Sample Number (IGSN), upload measurements directly from field instruments, and arrange sample curation. Available data display tools such as GeoMapApp and existing data sets (PetDB, IRIS, UNAVCO) could be integrated to direct locations for additional measurements during a field campaign. Nearly live display of sampling locations, pictures, and comments could be used as an educational and outreach tool during sampling expeditions. Achieving these goals requires the integration of existing online data resources, with common access through a dedicated web portal.

Remote quantification of phycocyanin in potable water sources through an adaptive model

NASA Astrophysics Data System (ADS)

Song, Kaishan; Li, Lin; Tedesco, Lenore P.; Li, Shuai; Hall, Bob E.; Du, Jia

2014-09-01

Cyanobacterial blooms in water supply sources in both central Indiana USA (CIN) and South Australia (SA) are a cause of great concerns for toxin production and water quality deterioration. Remote sensing provides an effective approach for quick assessment of cyanobacteria through quantification of phycocyanin (PC) concentration. In total, 363 samples spanning a large variation of optically active constituents (OACs) in CIN and SA waters were collected during 24 field surveys. Concurrently, remote sensing reflectance spectra (Rrs) were measured. A partial least squares-artificial neural network (PLS-ANN) model, artificial neural network (ANN) and three-band model (TBM) were developed or tuned by relating the Rrs with PC concentration. Our results indicate that the PLS-ANN model outperformed the ANN and TBM with both the original spectra and simulated ESA/Sentinel-3/Ocean and Land Color Instrument (OLCI) and EO-1/Hyperion spectra. The PLS-ANN model resulted in a high coefficient of determination (R2) for CIN dataset (R2 = 0.92, R: 0.3-220.7 μg/L) and SA (R2 = 0.98, R: 0.2-13.2 μg/L). In comparison, the TBM model yielded an R2 = 0.77 and 0.94 for the CIN and SA datasets, respectively; while the ANN obtained an intermediate modeling accuracy (CIN: R2 = 0.86; SA: R2 = 0.95). Applying the simulated OLCI and Hyperion aggregated datasets, the PLS-ANN model still achieved good performance (OLCI: R2 = 0.84; Hyperion: R2 = 0.90); the TBM also presented acceptable performance for PC estimations (OLCI: R2 = 0.65, Hyperion: R2 = 0.70). Based on the results, the PLS-ANN is an effective modeling approach for the quantification of PC in productive water supplies based on its effectiveness in solving the non-linearity of PC with other OACs. Furthermore, our investigation indicates that the ratio of inorganic suspended matter (ISM) to PC concentration has close relationship to modeling relative errors (CIN: R2 = 0.81; SA: R2 = 0.92), indicating that ISM concentration exert significant impact on PC estimation accuracy.

Spatial Variability in Column CO2 Inferred from High Resolution GEOS-5 Global Model Simulations: Implications for Remote Sensing and Inversions

NASA Technical Reports Server (NTRS)

Ott, L.; Putman, B.; Collatz, J.; Gregg, W.

2012-01-01

Column CO2 observations from current and future remote sensing missions represent a major advancement in our understanding of the carbon cycle and are expected to help constrain source and sink distributions. However, data assimilation and inversion methods are challenged by the difference in scale of models and observations. OCO-2 footprints represent an area of several square kilometers while NASA s future ASCENDS lidar mission is likely to have an even smaller footprint. In contrast, the resolution of models used in global inversions are typically hundreds of kilometers wide and often cover areas that include combinations of land, ocean and coastal areas and areas of significant topographic, land cover, and population density variations. To improve understanding of scales of atmospheric CO2 variability and representativeness of satellite observations, we will present results from a global, 10-km simulation of meteorology and atmospheric CO2 distributions performed using NASA s GEOS-5 general circulation model. This resolution, typical of mesoscale atmospheric models, represents an order of magnitude increase in resolution over typical global simulations of atmospheric composition allowing new insight into small scale CO2 variations across a wide range of surface flux and meteorological conditions. The simulation includes high resolution flux datasets provided by NASA s Carbon Monitoring System Flux Pilot Project at half degree resolution that have been down-scaled to 10-km using remote sensing datasets. Probability distribution functions are calculated over larger areas more typical of global models (100-400 km) to characterize subgrid-scale variability in these models. Particular emphasis is placed on coastal regions and regions containing megacities and fires to evaluate the ability of coarse resolution models to represent these small scale features. Additionally, model output are sampled using averaging kernels characteristic of OCO-2 and ASCENDS measurement concepts to create realistic pseudo-datasets. Pseudo-data are averaged over coarse model grid cell areas to better understand the ability of measurements to characterize CO2 distributions and spatial gradients on both short (daily to weekly) and long (monthly to seasonal) time scales

Developing GIS-based eastern equine encephalitis vector-host models in Tuskegee, Alabama.

PubMed

Jacob, Benjamin G; Burkett-Cadena, Nathan D; Luvall, Jeffrey C; Parcak, Sarah H; McClure, Christopher J W; Estep, Laura K; Hill, Geoffrey E; Cupp, Eddie W; Novak, Robert J; Unnasch, Thomas R

2010-02-24

A site near Tuskegee, Alabama was examined for vector-host activities of eastern equine encephalomyelitis virus (EEEV). Land cover maps of the study site were created in ArcInfo 9.2 from QuickBird data encompassing visible and near-infrared (NIR) band information (0.45 to 0.72 microm) acquired July 15, 2008. Georeferenced mosquito and bird sampling sites, and their associated land cover attributes from the study site, were overlaid onto the satellite data. SAS 9.1.4 was used to explore univariate statistics and to generate regression models using the field and remote-sampled mosquito and bird data. Regression models indicated that Culex erracticus and Northern Cardinals were the most abundant mosquito and bird species, respectively. Spatial linear prediction models were then generated in Geostatistical Analyst Extension of ArcGIS 9.2. Additionally, a model of the study site was generated, based on a Digital Elevation Model (DEM), using ArcScene extension of ArcGIS 9.2. For total mosquito count data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 5.041 km, nugget of 6.325 km, lag size of 7.076 km, and range of 31.43 km, using 12 lags. For total adult Cx. erracticus count, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 5.764 km, nugget of 6.114 km, lag size of 7.472 km, and range of 32.62 km, using 12 lags. For the total bird count data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 4.998 km, nugget of 5.413 km, lag size of 7.549 km and range of 35.27 km, using 12 lags. For the Northern Cardinal count data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 6.387 km, nugget of 5.935 km, lag size of 8.549 km and a range of 41.38 km, using 12 lags. Results of the DEM analyses indicated a statistically significant inverse linear relationship between total sampled mosquito data and elevation (R2 = -.4262; p < .0001), with a standard deviation (SD) of 10.46, and total sampled bird data and elevation (R2 = -.5111; p < .0001), with a SD of 22.97. DEM statistics also indicated a significant inverse linear relationship between total sampled Cx. erracticus data and elevation (R2 = -.4711; p < .0001), with a SD of 11.16, and the total sampled Northern Cardinal data and elevation (R2 = -.5831; p < .0001), SD of 11.42. These data demonstrate that GIS/remote sensing models and spatial statistics can capture space-varying functional relationships between field-sampled mosquito and bird parameters for determining risk for EEEV transmission.

Space Weathering of Ordinary Chondrite Parent Bodies, Its Impact on the Method of Distinguishing H, L, and LL Types and Implications for Itokawa Samples Returned by the Hayabusa Mission

NASA Technical Reports Server (NTRS)

Hiroi, T.; Sasaki, S.; Noble, S. K.; Pieters, C. M.

2011-01-01

As the most abundance meteorites in our collections, ordinary chondrites potentially have very important implications on the origin and formation of our Solar System. In order to map the distribution of ordinary chondrite-like asteroids through remote sensing, the space weathering effects of ordinary chondrite parent bodies must be addressed through experiments and modeling. Of particular importance is the impact on distinguishing different types (H/L/LL) of ordinary chondrites. In addition, samples of asteroid Itokawa returned by the Hayabusa spacecraft may re veal the mechanism of space weathering on an LLchondrite parent body. Results of space weathering simulations on ordinary chondrites and implications for Itokawa samples are presented here.

Remote electrochemical sensor

DOEpatents

Wang, J.; Olsen, K.; Larson, D.

1997-10-14

An electrochemical sensor is described for remote detection, particularly useful for metal contaminants and organic or other compounds. The sensor circumvents technical difficulties that previously prevented in-situ remote operations. The microelectrode, connected to a long communications cable, allows convenient measurements of the element or compound at timed and frequent intervals and instrument/sample distances of ten feet to more than 100 feet. The sensor is useful for both downhole groundwater monitoring and in-situ water (e.g., shipboard seawater) analysis. 21 figs.

ARE AIRBORNE CONTAMINANTS A RISK FACTOR TO AQUATIC ECOSYSTEMS IN REMOTE WESTERN NATIONAL PARKS (USA)

EPA Science Inventory

The Western Airborne Contaminants Assessment Project (WACAP) was initiated in 2002 by the National Park Service to determine if airborne contaminants were having an impact on remote western ecosystems. Multiple sample media (snow, water, sediment, fish and terrestrial vegetation...

A Combined Remote LIBS and Raman Spectroscopic Study of Minerals

NASA Technical Reports Server (NTRS)

Hubble, H. W.; Ghosh, M.; Sharma, S. K.; Horton, K. A.; Lucey, P. G.; Angel, S. M.; Wiens, R. C.

2002-01-01

In this paper, we explore the use of remote LIBS combined with pulsed-laser Raman spectroscopy for mineral analysis at a distance of 10 meters. Samples analyzed include: carbonates (both biogenic and abiogenic), silicates, and sulfates. Additional information is contained in the original extended abstract.

Fates, Budgets, and Health Implications of Macondo Spill Volatile Hydrocarbons in the Ocean and Atmosphere of the Gulf of Mexico

NASA Astrophysics Data System (ADS)

Leifer, I.; Barletta, B.; Blake, D. R.; Blake, N. J.; Bradley, E. S.; Meinardi, S.; Lehr, B.; Luyendyk, B. P.; Roberts, D. A.; Rowland, F. S.

2010-12-01

The Macondo Oil Spill released unprecedented oil and gas to the ocean, estimated at 63000 bbl/day, which dispersed and dissolved during rise (Technical Flow Rate Team Report, 2010); yet, most of the oil reached the sea surface as oil slicks that then evolved due to weathering and dispersant application (Mass Balance Report, 2010). Remote sensing (near infrared imaging spectrometry) allowed quantification of thick surface oil, values of which were incorporated into an overall oil budget calculation. Remote sensing data, atmospheric samples, and numerical modeling, strongly suggest significant volatile loss during rise, yet measured atmospheric concentrations were high. Scaling atmospheric measurements to the total oil spill implies very high, extensive, and persistent levels of atmospheric petroleum hydrocarbon exposure with strong health implications to on-site workers and to coastal residents from wind advection.

The distributed production system of the SuperB project: description and results

NASA Astrophysics Data System (ADS)

Brown, D.; Corvo, M.; Di Simone, A.; Fella, A.; Luppi, E.; Paoloni, E.; Stroili, R.; Tomassetti, L.

2011-12-01

The SuperB experiment needs large samples of MonteCarlo simulated events in order to finalize the detector design and to estimate the data analysis performances. The requirements are beyond the capabilities of a single computing farm, so a distributed production model capable of exploiting the existing HEP worldwide distributed computing infrastructure is needed. In this paper we describe the set of tools that have been developed to manage the production of the required simulated events. The production of events follows three main phases: distribution of input data files to the remote site Storage Elements (SE); job submission, via SuperB GANGA interface, to all available remote sites; output files transfer to CNAF repository. The job workflow includes procedures for consistency checking, monitoring, data handling and bookkeeping. A replication mechanism allows storing the job output on the local site SE. Results from 2010 official productions are reported.

Next-generation Strategies for Human Lunar Sorties

NASA Technical Reports Server (NTRS)

Cohen, B. A.

2013-01-01

The science community has had success in remote field experiences using two distinctly different models for humans-in-the-loop: the Apollo Science Support team (science backroom), and the robotic exploration of Mars. In the Apollo experience, the science team helped train the crew, designed geologic traverses, and made real-time decisions by reviewing audio and video transmissions and providing recommendations for geologic sampling. In contrast, the Mars Exploration Rover (MER) and Mars Science Lab (MSL) missions have been conducted entirely robotically, with significant time delays between science- driven decisions and remote field activities. Distinctive operations methods and field methodologies were developed for MER/MSL [1,2] because of the reliance on the "backroom" science team (rather than astronaut crew members) to understand the surroundings. Additionally, data are relayed to the team once per day, giving the team many hours or even days to assimilate the data and decide on a plan of action.

Water saturation effects on elastic wave attenuation in porous rocks with aligned fractures

NASA Astrophysics Data System (ADS)

Amalokwu, Kelvin; Best, Angus I.; Sothcott, Jeremy; Chapman, Mark; Minshull, Tim; Li, Xiang-Yang

2014-05-01

Elastic wave attenuation anisotropy in porous rocks with aligned fractures is of interest to seismic remote sensing of the Earth's structure and to hydrocarbon reservoir characterization in particular. We investigated the effect of partial water saturation on attenuation in fractured rocks in the laboratory by conducting ultrasonic pulse-echo measurements on synthetic, silica-cemented, sandstones with aligned penny-shaped voids (fracture density of 0.0298 ± 0.0077), chosen to simulate the effect of natural fractures in the Earth according to theoretical models. Our results show, for the first time, contrasting variations in the attenuation (Q-1) of P and S waves with water saturation in samples with and without fractures. The observed Qs/Qp ratios are indicative of saturation state and the presence or absence of fractures, offering an important new possibility for remote fluid detection and characterization.

Directional radiance measurements: Challenges in the sampling of landscapes

NASA Technical Reports Server (NTRS)

Deering, D. W.

1994-01-01

Most earth surfaces, particularly those supporting natural vegetation ecosystems, constitute structurally and spectrally complex surfaces that are distinctly non-Lambertian reflectors. Obtaining meaningful measurements of the directional radiances of landscapes and obtaining estimates of the complete bidirectional reflectance distribution functions of ground targets with complex and variable landscape and radiometric features are challenging tasks. Reasons for the increased interest in directional radiance measurements are presented, and the issues that must be addressed when trying to acquire directional radiances for vegetated land surfaces from different types of remote sensing platforms are discussed. Priority research emphases are suggested, concerning field measurements of directional surface radiances and reflectances for future research. Primarily, emphasis must be given to the acquisition of more complete and directly associated radiometric and biometric parameter data sets that will empower the exploitation of the 'angular dimension' in remote sensing of vegetation through enabling the further development and rigorous validation of state of the art plant canopy models.

Modeling the Hydrological Regime of Turkana Lake (Kenya, Ethiopia) by Combining Spatially Distributed Hydrological Modeling and Remote Sensing Datasets

NASA Astrophysics Data System (ADS)

Anghileri, D.; Kaelin, A.; Peleg, N.; Fatichi, S.; Molnar, P.; Roques, C.; Longuevergne, L.; Burlando, P.

2017-12-01

Hydrological modeling in poorly gauged basins can benefit from the use of remote sensing datasets although there are challenges associated with the mismatch in spatial and temporal scales between catchment scale hydrological models and remote sensing products. We model the hydrological processes and long-term water budget of the Lake Turkana catchment, a transboundary basin between Kenya and Ethiopia, by integrating several remote sensing products into a spatially distributed and physically explicit model, Topkapi-ETH. Lake Turkana is the world largest desert lake draining a catchment of 145'500 km2. It has three main contributing rivers: the Omo river, which contributes most of the annual lake inflow, the Turkwel river, and the Kerio rivers, which contribute the remaining part. The lake levels have shown great variations in the last decades due to long-term climate fluctuations and the regulation of three reservoirs, Gibe I, II, and III, which significantly alter the hydrological seasonality. Another large reservoir is planned and may be built in the next decade, generating concerns about the fate of Lake Turkana in the long run because of this additional anthropogenic pressure and increasing evaporation driven by climate change. We consider different remote sensing datasets, i.e., TRMM-V7 for precipitation, MERRA-2 for temperature, as inputs to the spatially distributed hydrological model. We validate the simulation results with other remote sensing datasets, i.e., GRACE for total water storage anomalies, GLDAS-NOAH for soil moisture, ERA-Interim/Land for surface runoff, and TOPEX/Poseidon for satellite altimetry data. Results highlight how different remote sensing products can be integrated into a hydrological modeling framework accounting for their relative uncertainties. We also carried out simulations with the artificial reservoirs planned in the north part of the catchment and without any reservoirs, to assess their impacts on the catchment hydrological regime and the Lake Turkana level variability.

Modeling and Validation of Environmental Suitability for Schistosomiasis Transmission Using Remote Sensing

PubMed Central

Walz, Yvonne; Wegmann, Martin; Dech, Stefan; Vounatsou, Penelope; Poda, Jean-Noël; N'Goran, Eliézer K.; Utzinger, Jürg; Raso, Giovanna

2015-01-01

Background Schistosomiasis is the most widespread water-based disease in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and human water contact patterns. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. We investigated the potential of remote sensing to characterize habitat conditions of parasite and intermediate host snails and discuss the relevance for public health. Methodology We employed high-resolution remote sensing data, environmental field measurements, and ecological data to model environmental suitability for schistosomiasis-related parasite and snail species. The model was developed for Burkina Faso using a habitat suitability index (HSI). The plausibility of remote sensing habitat variables was validated using field measurements. The established model was transferred to different ecological settings in Côte d’Ivoire and validated against readily available survey data from school-aged children. Principal Findings Environmental suitability for schistosomiasis transmission was spatially delineated and quantified by seven habitat variables derived from remote sensing data. The strengths and weaknesses highlighted by the plausibility analysis showed that temporal dynamic water and vegetation measures were particularly useful to model parasite and snail habitat suitability, whereas the measurement of water surface temperature and topographic variables did not perform appropriately. The transferability of the model showed significant relations between the HSI and infection prevalence in study sites of Côte d’Ivoire. Conclusions/Significance A predictive map of environmental suitability for schistosomiasis transmission can support measures to gain and sustain control. This is particularly relevant as emphasis is shifting from morbidity control to interrupting transmission. Further validation of our mechanistic model needs to be complemented by field data of parasite- and snail-related fitness. Our model provides a useful tool to monitor the development of new hotspots of potential schistosomiasis transmission based on regularly updated remote sensing data. PMID:26587839

Modeling and Validation of Environmental Suitability for Schistosomiasis Transmission Using Remote Sensing.

PubMed

Walz, Yvonne; Wegmann, Martin; Dech, Stefan; Vounatsou, Penelope; Poda, Jean-Noël; N'Goran, Eliézer K; Utzinger, Jürg; Raso, Giovanna

2015-11-01

Schistosomiasis is the most widespread water-based disease in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and human water contact patterns. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. We investigated the potential of remote sensing to characterize habitat conditions of parasite and intermediate host snails and discuss the relevance for public health. We employed high-resolution remote sensing data, environmental field measurements, and ecological data to model environmental suitability for schistosomiasis-related parasite and snail species. The model was developed for Burkina Faso using a habitat suitability index (HSI). The plausibility of remote sensing habitat variables was validated using field measurements. The established model was transferred to different ecological settings in Côte d'Ivoire and validated against readily available survey data from school-aged children. Environmental suitability for schistosomiasis transmission was spatially delineated and quantified by seven habitat variables derived from remote sensing data. The strengths and weaknesses highlighted by the plausibility analysis showed that temporal dynamic water and vegetation measures were particularly useful to model parasite and snail habitat suitability, whereas the measurement of water surface temperature and topographic variables did not perform appropriately. The transferability of the model showed significant relations between the HSI and infection prevalence in study sites of Côte d'Ivoire. A predictive map of environmental suitability for schistosomiasis transmission can support measures to gain and sustain control. This is particularly relevant as emphasis is shifting from morbidity control to interrupting transmission. Further validation of our mechanistic model needs to be complemented by field data of parasite- and snail-related fitness. Our model provides a useful tool to monitor the development of new hotspots of potential schistosomiasis transmission based on regularly updated remote sensing data.

Application of the remote-sensing communication model to a time-sensitive wildfire remote-sensing system

Treesearch

Christopher D. Lippitt; Douglas A. Stow; Philip J. Riggan

2016-01-01

Remote sensing for hazard response requires a priori identification of sensor, transmission, processing, and distribution methods to permit the extraction of relevant information in timescales sufficient to allow managers to make a given time-sensitive decision. This study applies and demonstrates the utility of the Remote Sensing Communication...

The Science and Application of Satellite Based Fire Radiative Energy

NASA Technical Reports Server (NTRS)

Ellicott, Evan; Vermote, Eric (Editor)

2012-01-01

The accurate measurement of ecosystem biomass is of great importance in scientific, resource management and energy sectors. In particular, biomass is a direct measurement of carbon storage within an ecosystem and of great importance for carbon cycle science and carbon emission mitigation. Remote Sensing is the most accurate tool for global biomass measurements because of the ability to measure large areas. Current biomass estimates are derived primarily from ground-based samples, as compiled and reported in inventories and ecosystem samples. By using remote sensing technologies, we are able to scale up the sample values and supply wall to wall mapping of biomass.

What would we miss if we characterized the Moon and Mars with just planetary meteorites, remote mapping, and robotic landers?. [Abstract only

NASA Technical Reports Server (NTRS)

Lindstrom, M. M.

1994-01-01

Exploration of the Moon and planets began with telescopic studies of their surfaces, continued with orbiting spacecraft and robotic landers, and will culminate with manned exploration and sample return. For the Moon and Mars we also have accidental samples provided by impacts on their surfaces, the lunar and martian meteorites. How much would we know about the lunar surface if we only had lunar meteorites, orbital spacecraft, and robotic exploration, and not the Apollo and Luna returned samples? What does this imply for Mars? With martian meteorites and data from Mariner, Viking, and the future Pathfinder missions, how much could we learn about Mars? The basis of most of our detailed knowledge about the Moon is the Apollo samples. They provide ground truth for the remote mapping, timescales for lunar processes, and samples from the lunar interior. The Moon is the foundation of planetary science and the basis for our interpretation of the other planets. Mars is similar to the Moon in that impact and volcanism are the dominant processes, but Mars' surface has also been affected by wind and water, and hence has much more complex surface geology. Future geochemical or mineralogical mapping of Mars' surface should be able to tell us whether the dominant rock types of the ancient southern highlands are basaltic, anorthositic, granitic, or something else, but will not be able to tell us the detailed mineralogy, geochemistry, or age. Without many more martian meteorites or returned samples we will not know the diversity of martian rocks, and therefore will be limited in our ability to model martian geological evolution.

A Review and Analysis of Remote Sensing Capability for Air Quality Measurements as a Potential Decision Support Tool Conducted by the NASA DEVELOP Program

NASA Technical Reports Server (NTRS)

Ross, A.; Richards, A.; Keith, K.; Frew, C.; Boseck, J.; Sutton, S.; Watts, C.; Rickman, D.

2007-01-01

This project focused on a comprehensive utilization of air quality model products as decision support tools (DST) needed for public health applications. A review of past and future air quality measurement methods and their uncertainty, along with the relationship of air quality to national and global public health, is vital. This project described current and future NASA satellite remote sensing and ground sensing capabilities and the potential for using these sensors to enhance the prediction, prevention, and control of public health effects that result from poor air quality. The qualitative uncertainty of current satellite remotely sensed air quality, the ground-based remotely sensed air quality, the air quality/public health model, and the decision making process is evaluated in this study. Current peer-reviewed literature suggests that remotely sensed air quality parameters correlate well with ground-based sensor data. A satellite remote-sensed and ground-sensed data complement is needed to enhance the models/tools used by policy makers for the protection of national and global public health communities

Networked remote area dental services: a viable, sustainable approach to oral health care in challenging environments.

PubMed

Dyson, Kate; Kruger, Estie; Tennant, Marc

2012-12-01

This study examines the cost effectiveness of a model of remote area oral health service. Retrospective financial analysis. Rural and remote primary health services. Clinical activity data and associated cost data relating to the provision of a networked visiting oral health service by the Centre for Rural and Remote Oral Health formed the basis of the study data frameset. The cost-effectiveness of the Centre's model of service provision at five rural and remote sites in Western Australia during the calendar years 2006, 2008 and 2010 was examined in the study. Calculations of the service provision costs and value of care provided were made using data records and the Fee Schedule of Dental Services for Dentists. The ratio of service provision costs to the value of care provided was determined for each site and was benchmarked against the equivalent ratios applicable to large scale government sector models of service provision. The use of networked models have been effective in other disciplines but this study is the first to show a networked hub and spoke approach of five spokes to one hub is cost efficient in remote oral health care. By excluding special cost-saving initiatives introduced by the Centre, the study examines easily translatable direct service provision costs against direct clinical care outcomes in some of Australia's most challenging locations. This study finds that networked hub and spoke models of care can be financially efficient arrangements in remote oral health care. © 2012 The Authors. Australian Journal of Rural Health © National Rural Health Alliance Inc.

[Hyperspectral remote sensing image classification based on SVM optimized by clonal selection].

PubMed

Liu, Qing-Jie; Jing, Lin-Hai; Wang, Meng-Fei; Lin, Qi-Zhong

2013-03-01

Model selection for support vector machine (SVM) involving kernel and the margin parameter values selection is usually time-consuming, impacts training efficiency of SVM model and final classification accuracies of SVM hyperspectral remote sensing image classifier greatly. Firstly, based on combinatorial optimization theory and cross-validation method, artificial immune clonal selection algorithm is introduced to the optimal selection of SVM (CSSVM) kernel parameter a and margin parameter C to improve the training efficiency of SVM model. Then an experiment of classifying AVIRIS in India Pine site of USA was performed for testing the novel CSSVM, as well as a traditional SVM classifier with general Grid Searching cross-validation method (GSSVM) for comparison. And then, evaluation indexes including SVM model training time, classification overall accuracy (OA) and Kappa index of both CSSVM and GSSVM were all analyzed quantitatively. It is demonstrated that OA of CSSVM on test samples and whole image are 85.1% and 81.58, the differences from that of GSSVM are both within 0.08% respectively; And Kappa indexes reach 0.8213 and 0.7728, the differences from that of GSSVM are both within 0.001; While the ratio of model training time of CSSVM and GSSVM is between 1/6 and 1/10. Therefore, CSSVM is fast and accurate algorithm for hyperspectral image classification and is superior to GSSVM.

Combined LIBS-Raman for remote detection and characterization of biological samples

DOE PAGES

Anderson, Aaron S.; Mukundan, Harshini; Mcinroy, Rhonda E.; ...

2015-02-07

Laser-Induced Breakdown Spectroscopy (LIBS) and Raman Spectroscopy have rich histories in the analysis of a wide variety of samples in both in situ and remote configurations. Our team is working on building a deployable, integrated Raman and LIBS spectrometer (RLS) for the parallel elucidation of elemental and molecular signatures under Earth and Martian surface conditions. Herein, results from remote LIBS and Raman analysis of biological samples such as amino acids, small peptides, mono- and disaccharides, and nucleic acids acquired under terrestrial and Mars conditions are reported, giving rise to some interesting differences. A library of spectra and peaks of interestmore » were compiled, and will be used to inform the analysis of more complex systems, such as large peptides, dried bacterial spores, and biofilms. Lastly, these results will be presented and future applications will be discussed, including the assembly of a combined RLS spectroscopic system and stand-off detection in a variety of environments.« less

Ellipsoids for anomaly detection in remote sensing imagery

NASA Astrophysics Data System (ADS)

Grosklos, Guenchik; Theiler, James

2015-05-01

For many target and anomaly detection algorithms, a key step is the estimation of a centroid (relatively easy) and a covariance matrix (somewhat harder) that characterize the background clutter. For a background that can be modeled as a multivariate Gaussian, the centroid and covariance lead to an explicit probability density function that can be used in likelihood ratio tests for optimal detection statistics. But ellipsoidal contours can characterize a much larger class of multivariate density function, and the ellipsoids that characterize the outer periphery of the distribution are most appropriate for detection in the low false alarm rate regime. Traditionally the sample mean and sample covariance are used to estimate ellipsoid location and shape, but these quantities are confounded both by large lever-arm outliers and non-Gaussian distributions within the ellipsoid of interest. This paper compares a variety of centroid and covariance estimation schemes with the aim of characterizing the periphery of the background distribution. In particular, we will consider a robust variant of the Khachiyan algorithm for minimum-volume enclosing ellipsoid. The performance of these different approaches is evaluated on multispectral and hyperspectral remote sensing imagery using coverage plots of ellipsoid volume versus false alarm rate.

A comparison of operational remote sensing-based models for estimating crop evapotranspiration

USDA-ARS?s Scientific Manuscript database

The integration of remotely sensed data into models of actual evapotranspiration has allowed for the estimation of water consumption across agricultural regions. Two modeling approaches have been successfully applied. The first approach computes a surface energy balance using the radiometric surface...

Quantitative Structure – Property Relationship Modeling of Remote Liposome Loading Of Drugs

PubMed Central

Cern, Ahuva; Golbraikh, Alexander; Sedykh, Aleck; Tropsha, Alexander; Barenholz, Yechezkel; Goldblum, Amiram

2012-01-01

Remote loading of liposomes by trans-membrane gradients is used to achieve therapeutically efficacious intra-liposome concentrations of drugs. We have developed Quantitative Structure Property Relationship (QSPR) models of remote liposome loading for a dataset including 60 drugs studied in 366 loading experiments internally or elsewhere. Both experimental conditions and computed chemical descriptors were employed as independent variables to predict the initial drug/lipid ratio (D/L) required to achieve high loading efficiency. Both binary (to distinguish high vs. low initial D/L) and continuous (to predict real D/L values) models were generated using advanced machine learning approaches and five-fold external validation. The external prediction accuracy for binary models was as high as 91–96%; for continuous models the mean coefficient R2 for regression between predicted versus observed values was 0.76–0.79. We conclude that QSPR models can be used to identify candidate drugs expected to have high remote loading capacity while simultaneously optimizing the design of formulation experiments. PMID:22154932

Remote sensing of soil moisture using airborne hyperspectral data

USGS Publications Warehouse

Finn, M.; Lewis, M.; Bosch, D.; Giraldo, Mario; Yamamoto, K.; Sullivan, D.; Kincaid, R.; Luna, R.; Allam, G.; Kvien, Craig; Williams, M.

2011-01-01

Landscape assessment of soil moisture is critical to understanding the hydrological cycle at the regional scale and in broad-scale studies of biophysical processes affected by global climate changes in temperature and precipitation. Traditional efforts to measure soil moisture have been principally restricted to in situ measurements, so remote sensing techniques are often employed. Hyperspectral sensors with finer spatial resolution and narrow band widths may offer an alternative to traditional multispectral analysis of soil moisture, particularly in landscapes with high spatial heterogeneity. This preliminary research evaluates the ability of remotely sensed hyperspectral data to quantify soil moisture for the Little River Experimental Watershed (LREW), Georgia. An airborne hyperspectral instrument with a short-wavelength infrared (SWIR) sensor was flown in 2005 and 2007 and the results were correlated to in situ soil moisture values. A significant statistical correlation (R2 value above 0.7 for both sampling dates) for the hyperspectral instrument data and the soil moisture probe data at 5.08 cm (2 inches) was determined. While models for the 20.32 cm (8 inches) and 30.48 cm (12 inches) depths were tested, they were not able to estimate soil moisture to the same degree.

Remote sensing of soil moisture using airborne hyperspectral data

USGS Publications Warehouse

Finn, Michael P.; Lewis, Mark (David); Bosch, David D.; Giraldo, Mario; Yamamoto, Kristina H.; Sullivan, Dana G.; Kincaid, Russell; Luna, Ronaldo; Allam, Gopala Krishna; Kvien, Craig; Williams, Michael S.

2011-01-01

Landscape assessment of soil moisture is critical to understanding the hydrological cycle at the regional scale and in broad-scale studies of biophysical processes affected by global climate changes in temperature and precipitation. Traditional efforts to measure soil moisture have been principally restricted to in situ measurements, so remote sensing techniques are often employed. Hyperspectral sensors with finer spatial resolution and narrow band widths may offer an alternative to traditional multispectral analysis of soil moisture, particularly in landscapes with high spatial heterogeneity. This preliminary research evaluates the ability of remotely sensed hyperspectral data to quantify soil moisture for the Little River Experimental Watershed (LREW), Georgia. An airborne hyperspectral instrument with a short-wavelength infrared (SWIR) sensor was flown in 2005 and 2007 and the results were correlated to in situ soil moisture values. A significant statistical correlation (R 2 value above 0.7 for both sampling dates) for the hyperspectral instrument data and the soil moisture probe data at 5.08 cm (2 inches) was determined. While models for the 20.32 cm (8 inches) and 30.48 cm (12 inches) depths were tested, they were not able to estimate soil moisture to the same degree.

A graphite oxide (GO)-based remote readable tamper evident seal

DOE PAGES

Cattaneo, Alessandro; Bossert, Jason Andrew; Guzman, Christian; ...

2016-09-08

Here, this paper presents a prototype of a remotely readable graphite oxide (GO) paper-based tamper evident seal. The proposed device combines the tunable electrical properties offered by reduced graphite oxide (RGO) with a compressive sampling scheme. The benefit of using RGO as a tamper evident seal material is the sensitivity of its electrical properties to the common mechanisms adopted to defeat tamper-evident seals. RGO’s electrical properties vary upon local stress or cracks induced by mechanical action (e.g., produced by shimming or lifting attacks). Further, modification of the seal’s electrical properties can result from the incidence of other defeat mechanisms, suchmore » as temperature changes, solvent treatment and steam application. The electrical tunability of RGO enables the engraving of a circuit on the area of the tamper evident seal intended to be exposed to malicious attacks. The operation of the tamper evident seal, as well as its remote communication functionality, is supervised by a microcontroller unit (MCU). The MCU uses the RGO-engraved circuitry to physically implement a compressive sampling acquisition procedure. The compressive sampling scheme provides the seal with self-authentication and self-state-of-health awareness capabilities. Finally, the prototype shows potential for use in low-power, embedded, remote-operation nonproliferation security related applications.« less

Microplastic pollution of lakeshore sediments from remote lakes in Tibet plateau, China.

PubMed

Zhang, Kai; Su, Jing; Xiong, Xiong; Wu, Xiang; Wu, Chenxi; Liu, Jiantong

2016-12-01

Tibetan Plateau is known as the world's third pole, which is characterized by a low population density with very limited human activities. Tibetan Plateau possesses the greatest numbers of high-altitude inland lakes in the world. However, no information is currently available on the characteristic of microplastic pollution in those lakes within this remote area. In this work, lakeshore sediments from four lakes within the Siling Co basin in northern Tibet were sampled and examined for microplastics (<5 mm). Microplastics were detected in six out of seven sampling sites with abundances ranging from 8 ± 14 to 563 ± 1219 items/m 2 . Riverine input might have contributed to the high abundance of microplastics observed in this remote area. Morphological features suggest that microplastics are derived from the breakdown of daily used plastic products. Polyethylene, polypropylene, polystyrene, polyethylene terephthalate, and polyvinyl chloride were identified from the microplastic samples using laser Raman spectroscopy, and oxidative and mechanical weathering textures were observed on the surface of microplastics using scanning electron microscope. These results demonstrate the presence of microplastics even for inland lakes in remote areas under very low human impact, and microplastic pollution can be a global issue. Copyright © 2016 Elsevier Ltd. All rights reserved.

Impact of remote sensing upon the planning, management, and development of water resources

NASA Technical Reports Server (NTRS)

Castruccio, P. A.; Loats, H. L.; Fowler, T. R.; Frech, S. L.

1975-01-01

Principal water resources users were surveyed to determine the impact of remote data streams on hydrologic computer models. Analysis of responses demonstrated that: most water resources effort suitable to remote sensing inputs is conducted through federal agencies or through federally stimulated research; and, most hydrologic models suitable to remote sensing data are federally developed. Computer usage by major water resources users was analyzed to determine the trends of usage and costs for the principal hydrologic users/models. The laws and empirical relationships governing the growth of the data processing loads were described and applied to project the future data loads. Data loads for ERTS CCT image processing were computed and projected through the 1985 era.

Assimilating Leaf Area Index Estimates from Remote Sensing into the Simulations of a Cropping Systems Model

USDA-ARS?s Scientific Manuscript database

Spatial extrapolation of cropping systems models for regional crop growth and water use assessment and farm-level precision management has been limited by the vast model input requirements and the model sensitivity to parameter uncertainty. Remote sensing has been proposed as a viable source of spat...

A hybrid approach to estimating national scale spatiotemporal variability of PM2.5 in the contiguous United States.

PubMed

Beckerman, Bernardo S; Jerrett, Michael; Serre, Marc; Martin, Randall V; Lee, Seung-Jae; van Donkelaar, Aaron; Ross, Zev; Su, Jason; Burnett, Richard T

2013-07-02

Airborne fine particulate matter exhibits spatiotemporal variability at multiple scales, which presents challenges to estimating exposures for health effects assessment. Here we created a model to predict ambient particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5) across the contiguous United States to be applied to health effects modeling. We developed a hybrid approach combining a land use regression model (LUR) selected with a machine learning method, and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals. The PM2.5 data set included 104,172 monthly observations at 1464 monitoring locations with approximately 10% of locations reserved for cross-validation. LUR models were based on remote sensing estimates of PM2.5, land use and traffic indicators. Normalized cross-validated R(2) values for LUR were 0.63 and 0.11 with and without remote sensing, respectively, suggesting remote sensing is a strong predictor of ground-level concentrations. In the models including the BME interpolation of the residuals, cross-validated R(2) were 0.79 for both configurations; the model without remotely sensed data described more fine-scale variation than the model including remote sensing. Our results suggest that our modeling framework can predict ground-level concentrations of PM2.5 at multiple scales over the contiguous U.S.

Environmental DNA sampling protocol - filtering water to capture DNA from aquatic organisms

USGS Publications Warehouse

Laramie, Matthew B.; Pilliod, David S.; Goldberg, Caren S.; Strickler, Katherine M.

2015-09-29

Environmental DNA (eDNA) analysis is an effective method of determining the presence of aquatic organisms such as fish, amphibians, and other taxa. This publication is meant to guide researchers and managers in the collection, concentration, and preservation of eDNA samples from lentic and lotic systems. A sampling workflow diagram and three sampling protocols are included as well as a list of suggested supplies. Protocols include filter and pump assembly using: (1) a hand-driven vacuum pump, ideal for sample collection in remote sampling locations where no electricity is available and when equipment weight is a primary concern; (2) a peristaltic pump powered by a rechargeable battery-operated driver/drill, suitable for remote sampling locations when weight consideration is less of a concern; (3) a 120-volt alternating current (AC) powered peristaltic pump suitable for any location where 120-volt AC power is accessible, or for roadside sampling locations. Images and detailed descriptions are provided for each step in the sampling and preservation process.

Development and evaluation of a water level proportional water sampler

NASA Astrophysics Data System (ADS)

Schneider, P.; Lange, A.; Doppler, T.

2013-12-01

We developed and adapted a new type of sampler for time-integrated, water level proportional water quality sampling (e.g. nutrients, contaminants and stable isotopes). Our samplers are designed for sampling small to mid-size streams based on the law of Hagen-Poiseuille, where a capillary (or a valve) limits the sampling aliquot by reducing the air flux out of a submersed plastic (HDPE) sampling container. They are good alternatives to battery-operated automated water samplers when working in remote areas, or at streams that are characterized by pronounced daily discharge variations such as glacier streams. We evaluated our samplers against standard automated water samplers (ISCO 2900 and ISCO 6712) during the snowmelt in the Black Forest and the Alps and tested them in remote glacial catchments in Iceland, Switzerland and Kyrgyzstan. The results clearly showed that our samplers are an adequate tool for time-integrated, water level proportional water sampling at remote test sites, as they do not need batteries, are relatively inexpensive, lightweight, and compact. They are well suited for headwater streams - especially when sampling for stable isotopes - as the sampled water is perfectly protected against evaporation. Moreover, our samplers have a reduced risk of icing in cold environments, as they are installed submersed in water, whereas automated samplers (typically installed outside the stream) may get clogged due to icing of hoses. Based on this study, we find these samplers to be an adequate replacement for automated samplers when time-integrated sampling or solute load estimates are the main monitoring tasks.

Estimation of ambient BVOC emissions using remote sensing techniques

NASA Astrophysics Data System (ADS)

Nichol, Janet; Wong, Man Sing

2011-06-01

The contribution of Biogenic Volatile Organic Compounds (BVOCs) to local air quality modelling is often ignored due to the difficulty of obtaining accurate spatial estimates of emissions. Yet their role in the formation of secondary aerosols and photochemical smog is thought to be significant, especially in hot tropical cities such as Hong Kong, which are situated downwind from dense forests. This paper evaluates Guenther et al.'s [Guenther, A., Hewitt, C.N., Erickson, D., Fall, R., Geron, C., Graedel, T.E., Harley, P., Klinger, L., Lerdau, M., McKay, W.A., Pierce, T., Scholes, B., Steinbrecher, R., Tallamraju, R., Taylor, J., Zimmerman, P., 1995. A global model of natural volatile organic compound emissions. Journal of Geophysical Research 100, 8873-8892] global model of BVOC emissions, for application at a spatially detailed level to Hong Kong's tropical forested landscape using high resolution remote sensing and ground data. The emission estimates are based on a landscape approach which assigns emission rates directly to ecosystem types not to individual species, since unlike in temperate regions where one or two single species may dominate over large regions, Hong Kong's vegetation is extremely diverse with up to 300 different species in one hectare. The resulting BVOC emission maps are suitable for direct input to regional and local air quality models giving 10 m raster output on an hourly basis over the whole of the Hong Kong territory, an area of 1100 km 2. Due to the spatially detailed mapping of isoprene emissions over the study area, it was possible to validate the model output using field data collected at a precise time and place by replicating those conditions in the model. The field measurement of emissions used for validating the model was based on a canister sampling technique, undertaken under different climatic conditions for Hong Kong's main ecosystem types in both urban and rural areas. The model-derived BVOC flux distributions appeared to be consistent with the field observations, indicating the robustness of the landscape modelling approach when applied to tropical forests at detailed level, as well as the promising role of remote sensing in BVOC mapping.

Meeting the challenge for effective antimicrobial stewardship programs in regional, rural and remote hospitals - what can we learn from the published literature?

PubMed

Bishop, Jaclyn; Kong, David Cm; Schulz, Thomas R; Thursky, Karin A; Buising, Kirsty L

2018-05-01

Antimicrobial resistance (AMR) has been recognised as an urgent health priority, both nationally and internationally. Australian hospitals are required to have an antimicrobial stewardship (AMS) program, yet the necessary resources may not be available in regional, rural or remote hospitals. This review will describe models for AMS programs that have been introduced in regional, rural or remote hospitals internationally and showcase achievements and key considerations that may guide Australian hospitals in establishing or sustaining AMS programs in the regional, rural or remote hospital setting. A narrative review was undertaken based on literature retrieved from searches in Ovid Medline, Scopus, Web of Science and the grey literature. 'Cited' and 'cited by' searches were undertaken to identify additional articles. Articles were included if they described an AMS program in the regional, rural or remote hospital setting (defined as a bed size less than 300 and located in a non-metropolitan setting). Eighteen articles were selected for inclusion. The AMS initiatives described were categorised into models designed to address two different challenges relating to AMS program delivery in regional, rural and remote hospitals. This included models to enable regional, rural and remote hospital staff to manage AMS programs in the absence of on-site infectious diseases (ID) trained experts. Non-ID doctor-led, pharmacist-led and externally led initiatives were identified. Lack of pharmacist resources was recognised as a core barrier to the further development of a pharmacist-led model. The second challenge was access to timely off-site expert ID clinical advice when required. Examples where this had been overcome included models utilising visiting ID specialists, telehealth and hospital network structures. Formalisation of such arrangements is important to clarify the accountabilities of all parties and enhance the quality of the service. Information technology was identified as a facilitator to a number of these models. The variance in availability of information technology between hospitals and cost limits the adoption of uniform programs to support AMS. Despite known barriers, regional, rural and remote hospitals have implemented AMS programs. The examples highlighted show that difficulty recruiting ID specialists should not inhibit AMS programs in regional, rural and remote hospitals, as much of the day-to-day work of AMS can be done by non-experts. Capacity building and the strengthening of networks are core features of these programs. Descriptions of how Australian regional, rural and remote hospitals have structured and supported their AMS programs would add to the existing body of knowledge sourced from international examples. Research into AMS programs predominantly led by GPs and nursing staff will provide further possible models for regional, rural and remote hospitals.

Remote Viewing and Computer Communications--An Experiment.

ERIC Educational Resources Information Center

Vallee, Jacques

1988-01-01

A series of remote viewing experiments were run with 12 participants who communicated through a computer conferencing network. The correct target sample was identified in 8 out of 33 cases. This represented more than double the pure chance expectation. Appendices present protocol, instructions, and results of the experiments. (Author/YP)

Update and review of accuracy assessment techniques for remotely sensed data

NASA Technical Reports Server (NTRS)

Congalton, R. G.; Heinen, J. T.; Oderwald, R. G.

1983-01-01

Research performed in the accuracy assessment of remotely sensed data is updated and reviewed. The use of discrete multivariate analysis techniques for the assessment of error matrices, the use of computer simulation for assessing various sampling strategies, and an investigation of spatial autocorrelation techniques are examined.

[Study on artificial neural network combined with multispectral remote sensing imagery for forest site evaluation].

PubMed

Gong, Yin-Xi; He, Cheng; Yan, Fei; Feng, Zhong-Ke; Cao, Meng-Lei; Gao, Yuan; Miao, Jie; Zhao, Jin-Long

2013-10-01

Multispectral remote sensing data containing rich site information are not fully used by the classic site quality evaluation system, as it merely adopts artificial ground survey data. In order to establish a more effective site quality evaluation system, a neural network model which combined remote sensing spectra factors with site factors and site index relations was established and used to study the sublot site quality evaluation in the Wangyedian Forest Farm in Inner Mongolia Province, Chifeng City. Based on the improved back propagation artificial neural network (BPANN), this model combined multispectral remote sensing data with sublot survey data, and took larch as example, Through training data set sensitivity analysis weak or irrelevant factor was excluded, the size of neural network was simplified, and the efficiency of network training was improved. This optimal site index prediction model had an accuracy up to 95.36%, which was 9.83% higher than that of the neural network model based on classic sublot survey data, and this shows that using multi-spectral remote sensing and small class survey data to determine the status of larch index prediction model has the highest predictive accuracy. The results fully indicate the effectiveness and superiority of this method.

36 CFR 9.82 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... may include aerial photography; remote sensing; hand-sampling of geologic materials; hand-sampling or....) and National Park Service policies concerning wilderness management and the use of motorized equipment...

Calibration of the MSL/ChemCam/LIBS Remote Sensing Composition Instrument

NASA Technical Reports Server (NTRS)

Wiens, R. C.; Maurice S.; Bender, S.; Barraclough, B. L.; Cousin, A.; Forni, O.; Ollila, A.; Newsom, H.; Vaniman, D.; Clegg, S.;

The Need for New Models for Delivery of Therapy Intervention to People with a Disability in Rural and Remote Areas of Australia

ERIC Educational Resources Information Center

Dew, Angela; Veitch, Craig; Lincoln, Michelle; Brentnall, Jennie; Bulkeley, Kim; Gallego, Gisselle; Bundy, Anita; Griffiths, Scott

2012-01-01

Therapy service delivery models to non-Indigenous and Indigenous people living in outer regional, remote, and very remote areas of Australia have typically involved irregular outreach from larger regional towns and capital cities. New South Wales (NSW) is the most populous Australian state with 7.23 million people of whom 4.58 million live in the…

Quantitative evaluation of water quality in the coastal zone by remote sensing

NASA Technical Reports Server (NTRS)

James, W. P.

1971-01-01

Remote sensing as a tool in a waste management program is discussed. By monitoring both the pollution sources and the environmental quality, the interaction between the components of the exturaine system was observed. The need for in situ sampling is reduced with the development of improved calibrated, multichannel sensors. Remote sensing is used for: (1) pollution source determination, (2) mapping the influence zone of the waste source on water quality parameters, and (3) estimating the magnitude of the water quality parameters. Diffusion coefficients and circulation patterns can also be determined by remote sensing, along with subtle changes in vegetative patterns and density.

Occurrence and seasonality of cyclic volatile methyl siloxanes in Arctic air.

PubMed

Krogseth, Ingjerd S; Kierkegaard, Amelie; McLachlan, Michael S; Breivik, Knut; Hansen, Kaj M; Schlabach, Martin

2013-01-02

Cyclic volatile methyl siloxanes (cVMS) are present in technical applications and personal care products. They are predicted to undergo long-range atmospheric transport, but measurements of cVMS in remote areas remain scarce. An active air sampling method for decamethylcyclopentasiloxane (D5) was further evaluated to include hexamethylcyclotrisiloxane (D3), octamethylcyclotetrasiloxane (D4), and dodecamethylcyclohexasiloxane (D6). Air samples were collected at the Zeppelin observatory in the remote Arctic (79° N, 12° E) with an average sampling time of 81 ± 23 h in late summer (August-October) and 25 ± 10 h in early winter (November-December) 2011. The average concentrations of D5 and D6 in late summer were 0.73 ± 0.31 and 0.23 ± 0.17 ng/m(3), respectively, and 2.94 ± 0.46 and 0.45 ± 0.18 ng/m(3) in early winter, respectively. Detection of D5 and D6 in the Arctic atmosphere confirms their long-range atmospheric transport. The D5 measurements agreed well with predictions from a Eulerian atmospheric chemistry-transport model, and seasonal variability was explained by the seasonality in the OH radical concentrations. These results extend our understanding of the atmospheric fate of D5 to high latitudes, but question the levels of D3 and D4 that have previously been measured at Zeppelin with passive air samplers.

Nanoscale Analysis of Space-Weathering Features in Soils from Itokawa

NASA Technical Reports Server (NTRS)

Thompson, M. S.; Christoffersen, R.; Zega, T. J.; Keller, L. P.

2014-01-01

Space weathering alters the spectral properties of airless body surface materials by redden-ing and darkening their spectra and attenuating characteristic absorption bands, making it challenging to characterize them remotely [1,2]. It also causes a discrepency between laboratory analysis of meteorites and remotely sensed spectra from asteroids, making it difficult to associate meteorites with their parent bodies. The mechanisms driving space weathering include mi-crometeorite impacts and the interaction of surface materials with solar energetic ions, particularly the solar wind. These processes continuously alter the microchemical and structural characteristics of exposed grains on airless bodies. The change of these properties is caused predominantly by the vapor deposition of reduced Fe and FeS nanoparticles (npFe(sup 0) and npFeS respectively) onto the rims of surface grains [3]. Sample-based analysis of space weathering has tra-ditionally been limited to lunar soils and select asteroidal and lunar regolith breccias [3-5]. With the return of samples from the Hayabusa mission to asteroid Itoka-wa [6], for the first time we are able to compare space-weathering features on returned surface soils from a known asteroidal body. Analysis of these samples will contribute to a more comprehensive model for how space weathering varies across the inner solar system. Here we report detailed microchemical and microstructal analysis of surface grains from Itokawa.

Flexible corner cube retroreflector array for temperature and strain sensing† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7ra13284k

PubMed Central

Khalid, Muhammad Waqas; Ahmed, Rajib; Yetisen, Ali K.

2018-01-01

Optical sensors for detecting temperature and strain play a crucial role in the analysis of environmental conditions and real-time remote sensing. However, the development of a single optical device that can sense temperature and strain simultaneously remains a challenge. Here, a flexible corner cube retroreflector (CCR) array based on passive dual optical sensing (temperature and strain) is demonstrated. A mechanical embossing process was utilised to replicate a three-dimensional (3D) CCR array in a soft flexible polymer film. The fabricated flexible CCR array samples were experimentally characterised through reflection measurements followed by computational modelling. As fabricated samples were illuminated with a monochromatic laser beam (635, 532, and 450 nm), a triangular shape reflection was obtained at the far-field. The fabricated flexible CCR array samples tuned retroreflected light based on external stimuli (temperature and strain as an applied force). For strain and temperature sensing, an applied force and temperature, in the form of weight suspension, and heat flow was applied to alter the replicated CCR surface structure, which in turn changed its optical response. Directional reflection from the heated flexible CCR array surface was also measured with tilt angle variation (max. up to 10°). Soft polymer CCRs may have potential in remote sensing applications, including measuring the temperature in space and in nuclear power stations. PMID:29568510

Remote sensing, hydrological modeling and in situ observations in snow cover research: A review

NASA Astrophysics Data System (ADS)

Dong, Chunyu

2018-06-01

Snow is an important component of the hydrological cycle. As a major part of the cryosphere, snow cover also represents a valuable terrestrial water resource. In the context of climate change, the dynamics of snow cover play a crucial role in rebalancing the global energy and water budgets. Remote sensing, hydrological modeling and in situ observations are three techniques frequently utilized for snow cover investigations. However, the uncertainties caused by systematic errors, scale gaps, and complicated snow physics, among other factors, limit the usability of these three approaches in snow studies. In this paper, an overview of the advantages, limitations and recent progress of the three methods is presented, and more effective ways to estimate snow cover properties are evaluated. The possibility of improving remotely sensed snow information using ground-based observations is discussed. As a rapidly growing source of volunteered geographic information (VGI), web-based geotagged photos have great potential to provide ground truth data for remotely sensed products and hydrological models and thus contribute to procedures for cloud removal, correction, validation, forcing and assimilation. Finally, this review proposes a synergistic framework for the future of snow cover research. This framework highlights the cross-scale integration of in situ and remotely sensed snow measurements and the assimilation of improved remote sensing data into hydrological models.

Remote infrared audible signage (RIAS) pilot program report.

DOT National Transportation Integrated Search

2011-07-01

The Remote Infrared Audible Sign Model Accessibility Program (RIAS MAP) is a program funded by the Federal Transit Administration (FTA) to evaluate the effectiveness of remote infrared audible sign systems in enabling persons with visual and cognitiv...

Modeling the Distribution of African Savanna Elephants in Kruger National Park: AN Application of Multi-Scale GLOBELAND30 Data

NASA Astrophysics Data System (ADS)

Xu, W.; Hays, B.; Fayrer-Hosken, R.; Presotto, A.

2016-06-01

The ability of remote sensing to represent ecologically relevant features at multiple spatial scales makes it a powerful tool for studying wildlife distributions. Species of varying sizes perceive and interact with their environment at differing scales; therefore, it is important to consider the role of spatial resolution of remotely sensed data in the creation of distribution models. The release of the Globeland30 land cover classification in 2014, with its 30 m resolution, presents the opportunity to do precisely that. We created a series of Maximum Entropy distribution models for African savanna elephants (Loxodonta africana) using Globeland30 data analyzed at varying resolutions. We compared these with similarly re-sampled models created from the European Space Agency's Global Land Cover Map (Globcover). These data, in combination with GIS layers of topography and distance to roads, human activity, and water, as well as elephant GPS collar data, were used with MaxEnt software to produce the final distribution models. The AUC (Area Under the Curve) scores indicated that the models created from 600 m data performed better than other spatial resolutions and that the Globeland30 models generally performed better than the Globcover models. Additionally, elevation and distance to rivers seemed to be the most important variables in our models. Our results demonstrate that Globeland30 is a valid alternative to the well-established Globcover for creating wildlife distribution models. It may even be superior for applications which require higher spatial resolution and less nuanced classifications.

Background element content of the lichen Pseudevernia furfuracea: A supra-national state of art implemented by novel field data from Italy.

PubMed

Cecconi, Elva; Incerti, Guido; Capozzi, Fiore; Adamo, Paola; Bargagli, Roberto; Benesperi, Renato; Candotto Carniel, Fabio; Favero-Longo, Sergio Enrico; Giordano, Simonetta; Puntillo, Domenico; Ravera, Sonia; Spagnuolo, Valeria; Tretiach, Mauro

2018-05-01

In biomonitoring, the knowledge of background element content (BEC) values is an essential pre-requisite for the correct assessment of pollution levels. Here, we estimated the BEC values of a highly performing biomonitor, the epiphytic lichen Pseudevernia furfuracea, by means of a careful review of literature data, integrated by an extensive field survey. Methodologically homogeneous element content datasets, reflecting different exposure conditions across European and extra-European countries, were compiled and comparatively analysed. Element content in samples collected in remote areas was compared to that of potentially enriched samples, testing differences between medians for 25 elements. This analysis confirmed that the former samples were substantially unaffected by anthropogenic contributions, and their metrics were therefore proposed as a first overview at supra-national background level. We also showed that bioaccumulation studies suffer a huge methodological variability. Limited to original field data, we investigated the background variability of 43 elements in 62 remote Italian sites, characterized in GIS environment for anthropization, land use, climate and lithology at different scale resolution. The relationships between selected environmental descriptors and BEC were tested using Principal Component Regression (PCR) modelling. Elemental composition resulted significantly dependent on land use, climate and lithology. In the case of lithogenic elements, regression models correctly reproduced the lichen content throughout the country at randomly selected sites. Further descriptors should be identified only for As, Co, and V. Through a multivariate approach we also identified three geographically homogeneous macro-regions for which specific BECs were provided for use as reference in biomonitoring applications. Copyright © 2017 Elsevier B.V. All rights reserved.

An international standard for observation data

NASA Astrophysics Data System (ADS)

Cox, Simon

2010-05-01

A generic information model for observations and related features supports data exchange both within and between different scientific and technical communities. Observations and Measurements (O&M) formalizes a neutral terminology for observation data and metadata. It was based on a model developed for medical observations, and draws on experience from geology and mineral exploration, in-situ monitoring, remote sensing, intelligence, biodiversity studies, ocean observations and climate simulations. Hundreds of current deployments of Sensor Observation Services (SOS), covering multiple disciplines, provide validation of the O&M model. A W3C Incubator group on 'Semantic Sensor Networks' is now using O&M as one of the bases for development of a formal ontology for sensor networks. O&M defines the information describing observation acts and their results, including the following key terms: observation, result, observed-property, feature-of-interest, procedure, phenomenon-time, and result-time. The model separates of the (meta-)data associated with the observation procedure, the observed feature, and the observation event itself. Observation results may take various forms, including scalar quantities, categories, vectors, grids, or any data structure required to represent the value of some property of some observed feature. O&M follows the ISO/TC 211 General Feature Model so non-geometric properties must be associated with typed feature instances. This requires formalization of information that may be trivial when working within some earth-science sub-disciplines (e.g. temperature, pressure etc. are associated with the atmosphere or ocean, and not just a location) but is critical to cross-disciplinary applications. It also allows the same structure and terminology to be used for in-situ, ex-situ and remote sensing observations, as well as for simulations. For example: a stream level observation is an in-situ monitoring application where the feature-of-interest is a reach, the observed property is water-level, and the result is a time-series of heights; stream quality is usually determined by ex-situ observation where the feature-of-interest is a specimen that is recovered from the stream, the observed property is water-quality, and the result is a set of measures of various parameters, or an assessment derived from these; on the other hand, distribution of surface temperature of a water body is typically determined through remote-sensing, where at observation time the procedure is located distant from the feature-of-interest, and the result is an image or grid. Observations usually involve sampling of an ultimate feature-of-interest. In the environmental sciences common sampling strategies are used. Spatial sampling is classified primarily by topological dimension (point, curve, surface, volume) and is supported by standard processing and visualisation tools. Specimens are used for ex-situ processing in most disciplines. Sampling features are often part of complexes (e.g. specimens are sub-divided; specimens are retrieved from points along a transect; sections are taken across tracts), so relationships between instances must be recorded. And observational campaigns involve collections of sampling features. The sampling feature model is a core part of O&M, and application experience has shown that describing the relationships between sampling features and observations is generally critical to successful use of the model. O&M was developed through Open Geospatial Consortium (OGC) as part of the Sensor Web Enablement (SWE) initiative. Other SWE standards include SensorML, SOS, Sensor Planning Service (SPS). The OGC O&M standard (Version 1) had two parts: part 1 describes observation events, and part 2 provides a schema sampling features. A revised version of O&M (Version 2) is to be published in a single document as ISO 19156. O&M Version 1 included an XML encoding for data exchange, which is used as the payload for SOS responses. The new version will provide a UML model only. Since an XML encoding may be generated following a rule, such as that presented in ISO 19136 (GML 3.2), it is not included in the standard directly. O&M Version 2 thus supports multiple physical implementations and versions.

Sample Acquisition and Handling System from a Remote Platform

NASA Technical Reports Server (NTRS)

Badescu, Mircea; Sherrit, Stewart; Jones, Jack A.

2011-01-01

A system has been developed to acquire and handle samples from a suspended remote platform. The system includes a penetrator, a penetrator deployment mechanism, and a sample handler. A gravity-driven harpoon sampler was used for the system, but other solutions can be used to supply the penetration energy, such as pyrotechnic, pressurized gas, or springs. The deployment mechanism includes a line that is attached to the penetrator, a spool for reeling in the line, and a line engagement control mechanism. The penetrator has removable tips that can collect liquid, ice, or solid samples. The handling mechanism consists of a carousel that can store a series of identical or different tips, assist in penetrator reconfiguration for multiple sample acquisition, and deliver the sample to a series of instruments for analysis. The carousel sample handling system was combined with a brassboard reeling mechanism and a penetrator with removable tips. It can attach the removable tip to the penetrator, release and retrieve the penetrator, remove the tip, and present it to multiple instrument stations. The penetrator can be remotely deployed from an aerobot, penetrate and collect the sample, and be retrieved with the sample to the aerobot. The penetrator with removable tips includes sample interrogation windows and a sample retainment spring for unconsolidated samples. The line engagement motor can be used to control the penetrator release and reeling engagement, and to evenly distribute the line on the spool by rocking between left and right ends of the spool. When the arm with the guiding ring is aligned with the spool axis, the line is free to unwind from the spool without rotating the spool. When the arm is perpendicular to the spool axis, the line can move only if the spool rotates.

The remote sensing image segmentation mean shift algorithm parallel processing based on MapReduce

NASA Astrophysics Data System (ADS)

Chen, Xi; Zhou, Liqing

2015-12-01

With the development of satellite remote sensing technology and the remote sensing image data, traditional remote sensing image segmentation technology cannot meet the massive remote sensing image processing and storage requirements. This article put cloud computing and parallel computing technology in remote sensing image segmentation process, and build a cheap and efficient computer cluster system that uses parallel processing to achieve MeanShift algorithm of remote sensing image segmentation based on the MapReduce model, not only to ensure the quality of remote sensing image segmentation, improved split speed, and better meet the real-time requirements. The remote sensing image segmentation MeanShift algorithm parallel processing algorithm based on MapReduce shows certain significance and a realization of value.

Potential for using remote sensing to estimate carbon fluxes across northern peatlands - A review.

PubMed

Lees, K J; Quaife, T; Artz, R R E; Khomik, M; Clark, J M

2018-02-15

Peatlands store large amounts of terrestrial carbon and any changes to their carbon balance could cause large changes in the greenhouse gas (GHG) balance of the Earth's atmosphere. There is still much uncertainty about how the GHG dynamics of peatlands are affected by climate and land use change. Current field-based methods of estimating annual carbon exchange between peatlands and the atmosphere include flux chambers and eddy covariance towers. However, remote sensing has several advantages over these traditional approaches in terms of cost, spatial coverage and accessibility to remote locations. In this paper, we outline the basic principles of using remote sensing to estimate ecosystem carbon fluxes and explain the range of satellite data available for such estimations, considering the indices and models developed to make use of the data. Past studies, which have used remote sensing data in comparison with ground-based calculations of carbon fluxes over Northern peatland landscapes, are discussed, as well as the challenges of working with remote sensing on peatlands. Finally, we suggest areas in need of future work on this topic. We conclude that the application of remote sensing to models of carbon fluxes is a viable research method over Northern peatlands but further work is needed to develop more comprehensive carbon cycle models and to improve the long-term reliability of models, particularly on peatland sites undergoing restoration. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Monitoring of "all-weather" evapotranspiration using optical and passive microwave remote sensing imagery over the River Source Region in Southwest China

NASA Astrophysics Data System (ADS)

Ma, Y.; Liu, S.

2017-12-01

Accurate estimation of surface evapotranspiration (ET) with high quality is one of the biggest obstacles for routine applications of remote sensing in eco-hydrological studies and water resource management at basin scale. However, many aspects urgently need to deeply research, such as the applicability of the ET models, the parameterization schemes optimization at the regional scale, the temporal upscaling, the selecting and developing of the spatiotemporal data fusion method and ground-based validation over heterogeneous land surfaces. This project is based on the theoretically robust surface energy balance system (SEBS) model, which the model mechanism need further investigation, including the applicability and the influencing factors, such as local environment, and heterogeneity of the landscape, for improving estimation accuracy. Due to technical and budget limitations, so far, optical remote sensing data is missing due to frequent cloud contamination and other poor atmospheric conditions in Southwest China. Here, a multi-source remote sensing data fusion method (ESTARFM: Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) method will be proposed through blending multi-source remote sensing data acquired by optical, and passive microwave remote sensors on board polar satellite platforms. The accurate "all-weather" ET estimation will be carried out for daily ET of the River Source Region in Southwest China, and then the remotely sensed ET results are overlapped with the footprint-weighted images of EC (eddy correlation) for ground-based validation.

Sampling Simulations for Assessing the Accuracy of U.S. Agricultural Crop Mapping from Remotely Sensed Imagery

NASA Astrophysics Data System (ADS)

Dwyer, Linnea; Yadav, Kamini; Congalton, Russell G.

2017-04-01

Providing adequate food and water for a growing, global population continues to be a major challenge. Mapping and monitoring crops are useful tools for estimating the extent of crop productivity. GFSAD30 (Global Food Security Analysis Data at 30m) is a program, funded by NASA, that is producing global cropland maps by using field measurements and remote sensing images. This program studies 8 major crop types, and includes information on cropland area/extent, if crops are irrigated or rainfed, and the cropping intensities. Using results from the US and the extensive reference data available, CDL (USDA Crop Data Layer), we will experiment with various sampling simulations to determine optimal sampling for thematic map accuracy assessment. These simulations will include varying the sampling unit, the sampling strategy, and the sample number. Results of these simulations will allow us to recommend assessment approaches to handle different cropping scenarios.

An integrated study of earth resources in the state of California using remote sensing techniques

NASA Technical Reports Server (NTRS)

Colwell, R. N. (Principal Investigator)

1975-01-01

The author has identified the following significant results. A weighted stratified double sample design using hardcopy LANDSAT-1 and ground data was utilized in developmental studies for snow water content estimation. Study results gave a correlation coefficient of 0.80 between LANDSAT sample units estimates of snow water content and ground subsamples. A basin snow water content estimate allowable error was given as 1.00 percent at the 99 percent confidence level with the same budget level utilized in conventional snow surveys. Several evapotranspiration estimation models were selected for efficient application at each level of data to be sampled. An area estimation procedure for impervious surface types of differing impermeability adjacent to stream channels was developed. This technique employs a double sample of 1:125,000 color infrared hightflight transparency data with ground or large scale photography.

Remote Sensing Technologies and Geospatial Modelling Hierarchy for Smart City Support

NASA Astrophysics Data System (ADS)

Popov, M.; Fedorovsky, O.; Stankevich, S.; Filipovich, V.; Khyzhniak, A.; Piestova, I.; Lubskyi, M.; Svideniuk, M.

2017-12-01

The approach to implementing the remote sensing technologies and geospatial modelling for smart city support is presented. The hierarchical structure and basic components of the smart city information support subsystem are considered. Some of the already available useful practical developments are described. These include city land use planning, urban vegetation analysis, thermal condition forecasting, geohazard detection, flooding risk assessment. Remote sensing data fusion approach for comprehensive geospatial analysis is discussed. Long-term city development forecasting by Forrester - Graham system dynamics model is provided over Kiev urban area.

Remote mass spectrometric sampling of electrospray- and desorption electrospray-generated ions using an air ejector.

PubMed

Dixon, R Brent; Bereman, Michael S; Muddiman, David C; Hawkridge, Adam M

2007-10-01

A commercial air ejector was coupled to an electrospray ionization linear ion trap mass spectrometer (LTQ) to transport remotely generated ions from both electrospray (ESI) and desorption electrospray ionization (DESI) sources. We demonstrate the remote analysis of a series of analyte ions that range from small molecules and polymers to polypeptides using the AE-LTQ interface. The details of the ESI-AE-LTQ and DESI-AE-LTQ experimental configurations are described and preliminary mass spectrometric data are presented.

Remote Mass Spectrometric Sampling of Electrospray- and Desorption Electrospray-Generated Ions Using an Air Ejector

PubMed Central

Dixon, R. Brent; Bereman, Michael S.; Muddiman, David C.; Hawkridge, Adam M.

2007-01-01

A commercial air ejector was coupled to an electrospray ionization linear ion trap mass spectrometer (LTQ) to transport remotely generated ions from both electrospray (ESI) and desorption electrospray ionization (DESI) sources. We demonstrate the remote analysis of a series of analyte ions that range from small molecules and polymers to polypeptides using the AE-LTQ interface. The details of the ESI-AE-LTQ and DESI-AE-LTQ experimental configurations are described and preliminary mass spectrometric data is presented. PMID:17716909

Near-earth orbital guidance and remote sensing

NASA Technical Reports Server (NTRS)

Powers, W. F.

1972-01-01

The curriculum of a short course in remote sensing and parameter optimization is presented. The subjects discussed are: (1) basics of remote sensing and the user community, (2) multivariant spectral analysis, (3) advanced mathematics and physics of remote sensing, (4) the atmospheric environment, (5) imaging sensing, and (6)nonimaging sensing. Mathematical models of optimization techniques are developed.

Remote in-situ laser-induced breakdown spectroscopy using optical fibers

NASA Astrophysics Data System (ADS)

Marquardt, Brian James

The following dissertation describes the development of methods for performing remote Laser-Induced Breakdown Spectroscopy (LIBS) using optical fibers. Studies were performed to determine the optimal excitation and collection parameters for remote LIBS measurements of glasses, soils and paint. A number of fiber-optic LIBS probes were developed and used to characterize various samples by plasma emission spectroscopy. A novel method for launching high-power laser pulses into optical fibers without causing catastrophic failure is introduced. A systematic study of a number of commercially available optical fibers was performed to determine which optical fibers were best suited for delivering high-power laser pulses. The general design of an all fiber-optic LIBS probe is described and applied to the determination of Pb in soil. A fiber-optic probe was developed for the microanalysis of solid samples remotely by LIBS, Raman spectroscopy and Raman imaging. The design of the probe allows for real-time sample imaging in-situ using coherent imaging fibers. This allows for precise atomic emission and Raman measurements to be performed remotely on samples in hostile or inaccessible environments. A novel technique was developed for collecting spectral plasma images using an acousto-optic tunable filter (AOTF). The spatial and temporal characteristics of the plasma were studied as a function of delay time. From the plasma images the distribution of Pb emission could be determined and fiber-optic designs could be optimized for signal collection. The performance of a two fiber LIBS probe is demonstrated for the determination of the amount of lead in samples of dry paint. It is shown that dry paint samples can be analyzed for their Pb content in-situ using a fiber-optic LIBS probe with detection limits well below the levels currently regulated by the Consumer Products Safety Commission. It is also shown that these measurements can be performed on both latex and enamel paints, and that Pb containing paint can be detected even under layers of non-lead containing paint. Experiments were performed to determine the optimal measurement parameters for performing LIBS studies of Department of Energy "waste" glasses. Calibration data for a Al and Ti metals contained in the waste glass is presented. The effects of laser power on plasma temperature, emission intensity and mass of sample ablated are introduced.

Earth Observation and Geospatial techniques for Soil Salinity and Land Capability Assessment over Sundarban Bay of Bengal Coast, India

NASA Astrophysics Data System (ADS)

Das, Sumanta; Choudhury, Malini Roy; Das, Subhasish; Nagarajan, M.

2016-12-01

To guarantee food security and job creation of small scale farmers to commercial farmers, unproductive farms in the South 24 PGS, West Bengal need land reform program to be restructured and evaluated for agricultural productivity. This study established a potential role of remote sensing and GIS for identification and mapping of salinity zone and spatial planning of agricultural land over the Basanti and Gosaba Islands(808.314sq. km) of South 24 PGS. District of West Bengal. The primary data i.e. soil pH, Electrical Conductivity (EC) and Sodium Absorption ratio (SAR) were obtained from soil samples of various GCP (Ground Control Points) locations collected at 50 mts. intervals by handheld GPS from 0-100 cm depths. The secondary information is acquired from the remotely sensed satellite data (LANDSAT ETM+) in different time scale and digital elevation model. The collected field samples were tested in the laboratory and were validated with Remote Sensing based digital indices analysisover the temporal satellite data to assess the potential changes due to over salinization. Soil physical properties such as texture, structure, depth and drainage condition is stored as attributes in a geographical soil database and linked with the soil map units. The thematic maps are integrated with climatic and terrain conditions of the area to produce land capability maps for paddy. Finally, The weighted overlay analysis was performed to assign theweights according to the importance of parameters taken into account for salineareaidentification and mapping to segregate higher, moderate, lower salinity zonesover the study area.

Using LiDAR data to measure the 3D green biomass of Beijing urban forest in China.

PubMed

He, Cheng; Convertino, Matteo; Feng, Zhongke; Zhang, Siyu

2013-01-01

The purpose of the paper is to find a new approach to measure 3D green biomass of urban forest and to testify its precision. In this study, the 3D green biomass could be acquired on basis of a remote sensing inversion model in which each standing wood was first scanned by Terrestrial Laser Scanner to catch its point cloud data, then the point cloud picture was opened in a digital mapping data acquisition system to get the elevation in an independent coordinate, and at last the individual volume captured was associated with the remote sensing image in SPOT5(System Probatoired'Observation dela Tarre)by means of such tools as SPSS (Statistical Product and Service Solutions), GIS (Geographic Information System), RS (Remote Sensing) and spatial analysis software (FARO SCENE and Geomagic studio11). The results showed that the 3D green biomass of Beijing urban forest was 399.1295 million m(3), of which coniferous was 28.7871 million m(3) and broad-leaf was 370.3424 million m(3). The accuracy of 3D green biomass was over 85%, comparison with the values from 235 field sample data in a typical sampling way. This suggested that the precision done by the 3D forest green biomass based on the image in SPOT5 could meet requirements. This represents an improvement over the conventional method because it not only provides a basis to evalue indices of Beijing urban greenings, but also introduces a new technique to assess 3D green biomass in other cities.

Using LiDAR Data to Measure the 3D Green Biomass of Beijing Urban Forest in China

PubMed Central

He, Cheng; Convertino, Matteo; Feng, Zhongke; Zhang, Siyu

2013-01-01

The purpose of the paper is to find a new approach to measure 3D green biomass of urban forest and to testify its precision. In this study, the 3D green biomass could be acquired on basis of a remote sensing inversion model in which each standing wood was first scanned by Terrestrial Laser Scanner to catch its point cloud data, then the point cloud picture was opened in a digital mapping data acquisition system to get the elevation in an independent coordinate, and at last the individual volume captured was associated with the remote sensing image in SPOT5(System Probatoired'Observation dela Tarre)by means of such tools as SPSS (Statistical Product and Service Solutions), GIS (Geographic Information System), RS (Remote Sensing) and spatial analysis software (FARO SCENE and Geomagic studio11). The results showed that the 3D green biomass of Beijing urban forest was 399.1295 million m3, of which coniferous was 28.7871 million m3 and broad-leaf was 370.3424 million m3. The accuracy of 3D green biomass was over 85%, comparison with the values from 235 field sample data in a typical sampling way. This suggested that the precision done by the 3D forest green biomass based on the image in SPOT5 could meet requirements. This represents an improvement over the conventional method because it not only provides a basis to evalue indices of Beijing urban greenings, but also introduces a new technique to assess 3D green biomass in other cities. PMID:24146792

Detecting trends in regional ecosystem functioning: the importance of field data for calibrating and validating NEON airborne remote sensing instruments and science data products

NASA Astrophysics Data System (ADS)

McCorkel, J.; Kuester, M. A.; Johnson, B. R.; Krause, K.; Kampe, T. U.; Moore, D. J.

2011-12-01

The National Ecological Observatory Network (NEON) is a research facility under development by the National Science Foundation to improve our understanding of and ability to forecast the impacts of climate change, land-use change, and invasive species on ecology. The infrastructure, designed to operate over 30 years or more, includes site-based flux tower and field measurements, coordinated with airborne remote sensing observations to observe key ecological processes over a broad range of temporal and spatial scales. NEON airborne data on vegetation biochemical, biophysical, and structural properties and on land use and land cover will be captured at 1 to 2 meter resolution by an imaging spectrometer, a small-footprint waveform-LiDAR and a high-resolution digital camera. Annual coverage of the 60 NEON sites and capacity to support directed research flights or respond to unexpected events will require three airborne observation platforms (AOP). The integration of field and airborne data with satellite observations and other national geospatial data for analysis, monitoring and input to ecosystem models will extend NEON observations to regions across the United States not directly sampled by the observatory. The different spatial scales and measurement methods make quantitative comparisons between remote sensing and field data, typically collected over small sample plots (e.g. < 0.2 ha), difficult. New approaches to developing temporal and spatial scaling relationships between these data are necessary to enable validation of airborne and satellite remote sensing data and for incorporation of these data into continental or global scale ecological models. In addition to consideration of the methods used to collect ground-based measurements, careful calibration of the remote sensing instrumentation and an assessment of the accuracy of algorithms used to derive higher-level science data products are needed. Furthermore, long-term consistency of the data collected by all three airborne instrument packages over the NEON sites requires traceability of the calibration to national standards, field-based verification of instrument calibration and stability in the aircraft environment, and an independent assessment of the quality of derived data products. This work describes the development of the calibration laboratory, early evaluation of field-based vicarious calibration, development of scaling relationships, and test flights. Complementary laboratory- and field-based calibration of the AOP in addition to consistency with on-board calibration methods provide confidence that low-level data such as radiance and surface reflectance measurements are accurate and comparable among different sensors. Algorithms that calculate higher-level data products including essential climate variables will be validated against equivalent ground- and satellite-based results. Such a validated data set across multiple spatial and temporal scales is key to enabling ecosystem models to forecast the effects of climate change, land-use change and invasive species on the continental scale.

Spatial Variations in CO2 Mixing Ratios Over a Heterogenous Landscape - Linking Airborne Measurements With Remote Sensing Derived Biophysical Parameters

NASA Astrophysics Data System (ADS)

Choi, Y.; Vadrevu, K. P.; Vay, S. A.; Woo, J.

2006-12-01

North American terrestrial ecosystems are major sources and sinks of carbon. Precise measurement of atmospheric CO2 concentrations plays an important role in the development and testing of carbon cycle models quantifying the influence of terrestrial CO2 exchange on the North American carbon budget. During the summer 2004 Intercontinental Chemical Transport Experiment North America (INTEX-NA) campaign, regional scale in-situ measurements of atmospheric CO2 were made from the NASA DC-8 affording the opportunity to explore how land surface heterogeneity relates to the airborne observations utilizing remote-sensing data products and GIS-based methods. These 1 Hz data reveal the seasonal biospheric uptake of CO2 over portions of the U.S. continent, especially east of 90°W below 2 km, compared to higher mixing ratios over water as well as within the upper troposphere where well-mixed, aged air masses were sampled. In this study, we use several remote sensing derived biophysical parameters from the LANDSAT, NOAA AVHRR, and MODIS sensors to specify spatiotemporal patterns of land use cover and vegetation characteristics for linking the airborne measurements of CO2 data with terrestrial sources of carbon. Also, CO2 flux footprint outputs from a 3-D Lagrangian atmospheric model have been integrated with satellite remote sensing data to infer CO2 variations across heterogeneous landscapes. In examining the landscape mosaic utilizing these available tools, preliminary results suggest that the lowest CO2 mixing ratios observed during INTEX-NA were over agricultural fields in Illinois dominated by corn then secondarily soybean crops. Low CO2 concentrations are attributable to sampling during the peak growing season over such C4 plants as corn having a higher photosynthetic rate via the C4-dicarboxylic acid pathway of carbon fixation compared to C3 plants such as soybeans. In addition to LANDSAT derived land cover data, results from comparisons of the airborne CO2 observations with vegetation indices of NDVI and EVI derived from MODIS data were used. Higher CO2 mixing ratios anti-correlated with the vegetation indices derived from MODIS data were observed. This is attributable to the photosynthetic uptake of CO2 by plants and convective mixing of the atmosphere. Details of satellite data characteristics related with the in-situ CO2 measurements will be presented.

Remote Sensing Soil Moisture Analysis by Unmanned Aerial Vehicles Digital Imaging

NASA Astrophysics Data System (ADS)

Yeh, C. Y.; Lin, H. R.; Chen, Y. L.; Huang, S. Y.; Wen, J. C.

2017-12-01

In recent years, remote sensing analysis has been able to apply to the research of climate change, environment monitoring, geology, hydro-meteorological, and so on. However, the traditional methods for analyzing wide ranges of surface soil moisture of spatial distribution surveys may require plenty resources besides the high cost. In the past, remote sensing analysis performed soil moisture estimates through shortwave, thermal infrared ray, or infrared satellite, which requires lots of resources, labor, and money. Therefore, the digital image color was used to establish the multiple linear regression model. Finally, we can find out the relationship between surface soil color and soil moisture. In this study, we use the Unmanned Aerial Vehicle (UAV) to take an aerial photo of the fallow farmland. Simultaneously, we take the surface soil sample from 0-5 cm of the surface. The soil will be baking by 110° C and 24 hr. And the software ImageJ 1.48 is applied for the analysis of the digital images and the hue analysis into Red, Green, and Blue (R, G, B) hue values. The correlation analysis is the result from the data obtained from the image hue and the surface soil moisture at each sampling point. After image and soil moisture analysis, we use the R, G, B and soil moisture to establish the multiple regression to estimate the spatial distributions of surface soil moisture. In the result, we compare the real soil moisture and the estimated soil moisture. The coefficient of determination (R2) can achieve 0.5-0.7. The uncertainties in the field test, such as the sun illumination, the sun exposure angle, even the shadow, will affect the result; therefore, R2 can achieve 0.5-0.7 reflects good effect for the in-suit test by using the digital image to estimate the soil moisture. Based on the outcomes of the research, using digital images from UAV to estimate the surface soil moisture is acceptable. However, further investigations need to be collected more than ten days (four times a day) data to verify the relation between the image hue and the soil moisture for reliable moisture estimated model. And it is better to use the digital single lens reflex camera to prevent the deformation of the image and to have a better auto exposure. Keywords: soil, moisture, remote sensing

A sampling device with a capped body and detachable handle

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

Jezek, Gerd-Rainer

1997-12-01

The present invention relates to a device for sampling radioactive waste and more particularly to a device for sampling radioactive waste which prevents contamination of a sampled material and the environment surrounding the sampled material. During vitrification of nuclear wastes, it is necessary to remove contamination from the surfaces of canisters filled with radioactive glass. After removal of contamination, a sampling device is used to test the surface of the canister. The one piece sampling device currently in use creates a potential for spreading contamination during vitrification operations. During operations, the one piece sampling device is transferred into and outmore » of the vitrification cell through a transfer drawer. Inside the cell, a remote control device handles the sampling device to wipe the surface of the canister. A one piece sampling device can be contaminated by the remote control device prior to use. Further, the sample device can also contaminate the transfer drawer producing false readings for radioactive material. The present invention overcomes this problem by enclosing the sampling pad in a cap. The removable handle is reused which reduces the amount of waste material.« less

Accessing and Utilizing Remote Sensing Data for Vectorborne Infectious Diseases Surveillance and Modeling

NASA Technical Reports Server (NTRS)

Kiang, Richard; Adimi, Farida; Kempler, Steven

2008-01-01

Background: The transmission of vectorborne infectious diseases is often influenced by environmental, meteorological and climatic parameters, because the vector life cycle depends on these factors. For example, the geophysical parameters relevant to malaria transmission include precipitation, surface temperature, humidity, elevation, and vegetation type. Because these parameters are routinely measured by satellites, remote sensing is an important technological tool for predicting, preventing, and containing a number of vectorborne infectious diseases, such as malaria, dengue, West Nile virus, etc. Methods: A variety of NASA remote sensing data can be used for modeling vectorborne infectious disease transmission. We will discuss both the well known and less known remote sensing data, including Landsat, AVHRR (Advanced Very High Resolution Radiometer), MODIS (Moderate Resolution Imaging Spectroradiometer), TRMM (Tropical Rainfall Measuring Mission), ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), EO-1 (Earth Observing One) ALI (Advanced Land Imager), and SIESIP (Seasonal to Interannual Earth Science Information Partner) dataset. Giovanni is a Web-based application developed by the NASA Goddard Earth Sciences Data and Information Services Center. It provides a simple and intuitive way to visualize, analyze, and access vast amounts of Earth science remote sensing data. After remote sensing data is obtained, a variety of techniques, including generalized linear models and artificial intelligence oriented methods, t 3 can be used to model the dependency of disease transmission on these parameters. Results: The processes of accessing, visualizing and utilizing precipitation data using Giovanni, and acquiring other data at additional websites are illustrated. Malaria incidence time series for some parts of Thailand and Indonesia are used to demonstrate that malaria incidences are reasonably well modeled with generalized linear models and artificial intelligence based techniques. Conclusions: Remote sensing data relevant to the transmission of vectorborne infectious diseases can be conveniently accessed at NASA and some other websites. These data are useful for vectorborne infectious disease surveillance and modeling.

Remote Sensing Soil Salinity Map for the San Joaquin Vally, California

NASA Astrophysics Data System (ADS)

Scudiero, E.; Skaggs, T. H.; Anderson, R. G.; Corwin, D. L.

2015-12-01

Soil salinization is a major natural hazard to worldwide agriculture. We present a remote imagery approach that maps salinity within a range (i.e., salinities less than 20 dS m-1, when measured as the electrical conductivity of the soil saturation extract), accuracy, and resolution most relevant to agriculture. A case study is presented for the western San Joaquin Valley (WSJV), California, USA (~870,000 ha of farmland) using multi-year Landsat 7 ETM+ canopy reflectance and the Canopy Response Salinity Index (CRSI). Highly detailed salinity maps for 22 fields (542 ha) established from apparent soil electrical conductivity directed sampling were used as ground-truth (sampled in 2013), totaling over 5000 pixels (30×30 m) with salinity values in the range of 0 to 35.2 dS m-1. Multi-year maximum values of CRSI were used to model soil salinity. In addition, soil type, elevation, meteorological data, and crop type were evaluated as covariates. The fitted model (R2=0.73) was validated: i) with a spatial k-folds (i.e., leave-one-field-out) cross-validation (R2=0.61), ii) versus salinity data from three independent fields (sampled in 2013 and 2014), and iii) by determining the accuracy of the qualitative classification of white crusted land as extremely-saline soils. The effect of land use change is evaluated over 2396 ha in the Broadview Water District from a comparison of salinity mapped in 1991 with salinity predicted in 2013 from the fitted model. From 1991 to 2013 salinity increased significantly over the selected study site, bringing attention to potential negative effects on soil quality of shifting from irrigated agriculture to fallow-land. This is cause for concern since over the 3 years of California's drought (2010-2013) the fallow land in the WSJV increased from 12.7% to 21.6%, due to drastic reduction in water allocations to farmers.

Structural and spectroscopic changes to natural nontronite induced by experimental impacts between 10 and 40 GPa

NASA Astrophysics Data System (ADS)

Friedlander, Lonia R.; Glotch, Timothy D.; Bish, David L.; Dyar, M. Darby; Sharp, Thomas G.; Sklute, Elizabeth C.; Michalski, Joseph R.

2015-05-01

Many phyllosilicate deposits remotely detected on Mars occur within bombarded terrains. Shock metamorphism from meteor impacts alters mineral structures, producing changed mineral spectra. Thus, impacts have likely affected the spectra of remotely sensed Martian phyllosilicates. We present spectral analysis results for a natural nontronite sample before and after laboratory-generated impacts over five peak pressures between 10 and 40 GPa. We conducted a suite of spectroscopic analyses to characterize the sample's impact-induced structural and spectral changes. Nontronite becomes increasingly disordered with increasing peak impact pressure. Every infrared spectroscopic technique used showed evidence of structural changes at shock pressures above ~25 GPa. Reflectance spectroscopy in the visible near-infrared region is primarily sensitive to the vibrations of metal-OH and interlayer H2O groups in the nontronite octahedral sheet. Midinfrared (MIR) spectroscopic techniques are sensitive to the vibrations of silicon and oxygen in the nontronite tetrahedral sheet. Because the tetrahedral and octahedral sheets of nontronite deform differently, impact-driven structural deformation may contribute to differences in phyllosilicate detection between remote sensing techniques sensitive to different parts of the nontronite structure. Observed spectroscopic changes also indicated that the sample's octahedral and tetrahedral sheets were structurally deformed but not completely dehydroxylated. This finding is an important distinction from previous studies of thermally altered phyllosilicates in which dehydroxylation follows dehydration in a stepwise progression preceding structural deformation. Impact alteration may thus complicate mineral-specific identifications based on the location of OH-group bands in remotely detected spectra. This is a key implication for Martian remote sensing arising from our results.

Moon-Mars simulation campaign in volcanic Eifel: Remote science support and sample analysis

NASA Astrophysics Data System (ADS)

Offringa, Marloes; Foing, Bernard H.; Kamps, Oscar

2016-07-01

Moon-Mars analogue missions using a mock-up lander that is part of the ESA/ILEWG ExoGeoLab project were conducted during Eifel field campaigns in 2009, 2015 and 2016 (Foing et al., 2010). In the last EuroMoonMars2016 campaign the lander was used to conduct reconnaissance experiments and in situ geological scientific analysis of samples, with a payload that mainly consisted of a telescope and a UV-VIS reflectance spectrometer. The aim of the campaign was to exhibit possibilities for the ExoGeoLab lander to perform remotely controlled experiments and test its applicability in the field by simulating the interaction with astronauts. The Eifel region in Germany where the experiments with the ExoGeoLab lander were conducted is a Moon-Mars analogue due to its geological setting and volcanic rock composition. The research conducted by analysis equipment on the lander could function in support of Moon-Mars sample return missions, by providing preliminary insight into characteristics of the analyzed samples. The set-up of the prototype lander was that of a telescope with camera and solar power equipment deployed on the top, the UV-VIS reflectance spectrometer together with computers and a sample webcam were situated in the middle compartment and to the side a sample analysis test bench was attached, attainable by astronauts from outside the lander. An alternative light source that illuminated the samples in case of insufficient daylight was placed on top of the lander and functioned on solar power. The telescope, teleoperated from a nearby stationed pressurized transport vehicle that functioned as a base control center, attained an overview of the sampling area and assisted the astronauts in their initial scouting pursuits. Locations of suitable sampling sites based on these obtained images were communicated to the astronauts, before being acquired during a simulated EVA. Sampled rocks and soils were remotely analyzed by the base control center, while the astronauts assisted by placing the samples onto the sample holder and adjusting test bench settings in order to obtain spectra. After analysis the collected samples were documented and stored by the astronauts, before returning to the base. Points of improvement for the EuroMoonMars2016 analog campaign are the remote control of the computers using an established network between the base and the lander. During following missions the computers should preferably be operated over a larger distance without interference. In the bottom compartment of the lander a rover is stored that in future campaigns could replace astronaut functions by collecting and returning samples, as well as performing adjustments to the analysis test bench by using a remotely controlled robotic arm. Acknowledgements: we thank Dominic Doyle for ESTEC optical lab support, Aidan Cowley (EAC) and Matthias Sperl (DLR) for support discussions, and collaborators from EuroMoonMars Eifel 2015-16 campaign team.

Capacity Model and Constraints Analysis for Integrated Remote Wireless Sensor and Satellite Network in Emergency Scenarios

PubMed Central

Zhang, Wei; Zhang, Gengxin; Dong, Feihong; Xie, Zhidong; Bian, Dongming

2015-01-01

This article investigates the capacity problem of an integrated remote wireless sensor and satellite network (IWSSN) in emergency scenarios. We formulate a general model to evaluate the remote sensor and satellite network capacity. Compared to most existing works for ground networks, the proposed model is time varying and space oriented. To capture the characteristics of a practical network, we sift through major capacity-impacting constraints and analyze the influence of these constraints. Specifically, we combine the geometric satellite orbit model and satellite tool kit (STK) engineering software to quantify the trends of the capacity constraints. Our objective in analyzing these trends is to provide insights and design guidelines for optimizing the integrated remote wireless sensor and satellite network schedules. Simulation results validate the theoretical analysis of capacity trends and show the optimization opportunities of the IWSSN. PMID:26593919

Challenges and solutions for applying the travel cost demand model to geographically remote visitor destinations: A case study of bear viewing at Katmai National Park and Preserve

USGS Publications Warehouse

Richardson, Leslie; Huber, Christopher; Loomis, John

2017-01-01

Remote and unique destinations present difficulties when attempting to construct traditional travel cost models to value recreation demand. The biggest limitation comes from the lack of variation in the dependent variable, defined as the number of trips taken over a set time frame. There are various approaches that can be used for overcoming limitations of the traditional travel cost model in the context of remote destinations. This study applies an adaptation of the standard model to estimate recreation benefits of bear viewing at Katmai National Park and Preserve in Alaska, which represents a once-in-a-lifetime experience for many visitors. Results demonstrate that visitors to this park’s Brooks Camp area are willing to pay an average of US$287 per day of bear viewing. Implications of these findings for valuing recreation at other remote destinations are discussed.

Capacity Model and Constraints Analysis for Integrated Remote Wireless Sensor and Satellite Network in Emergency Scenarios.

PubMed

Zhang, Wei; Zhang, Gengxin; Dong, Feihong; Xie, Zhidong; Bian, Dongming

2015-11-17

This article investigates the capacity problem of an integrated remote wireless sensor and satellite network (IWSSN) in emergency scenarios. We formulate a general model to evaluate the remote sensor and satellite network capacity. Compared to most existing works for ground networks, the proposed model is time varying and space oriented. To capture the characteristics of a practical network, we sift through major capacity-impacting constraints and analyze the influence of these constraints. Specifically, we combine the geometric satellite orbit model and satellite tool kit (STK) engineering software to quantify the trends of the capacity constraints. Our objective in analyzing these trends is to provide insights and design guidelines for optimizing the integrated remote wireless sensor and satellite network schedules. Simulation results validate the theoretical analysis of capacity trends and show the optimization opportunities of the IWSSN.

An intercomparison of three remote sensing-based energy balance models using large aperture scintillometer measurements over a wheat-corn production region

USDA-ARS?s Scientific Manuscript database

This paper compares three remote sensing-based models for estimating evapotranspiration (ET), namely the Surface Energy Balance System (SEBS), the Two-Source Energy Balance (TSEB) model, and the surface Temperature-Vegetation index Triangle (TVT). The models used as input MODIS/TERRA products and gr...

The application of remote sensing to the development and formulation of hydrologic planning models

NASA Technical Reports Server (NTRS)

Castruccio, P. A.; Loats, H. L., Jr.; Fowler, T. R.

1976-01-01

A hydrologic planning model is developed based on remotely sensed inputs. Data from LANDSAT 1 are used to supply the model's quantitative parameters and coefficients. The use of LANDSAT data as information input to all categories of hydrologic models requiring quantitative surface parameters for their effects functioning is also investigated.

A Retrospective Evaluation of Remote Pharmacist Interventions in a Telepharmacy Service Model Using a Conceptual Framework

PubMed Central

Murante, Lori J.; Moffett, Lisa M.

2014-01-01

Abstract Objectives: This retrospective cross-sectional study evaluated a telepharmacy service model using a conceptual framework to compare documented remote pharmacist interventions by year, hospital, and remote pharmacist and across rural hospitals with or without an on-site rural hospital pharmacist. Materials and Methods: Documented remote pharmacist interventions for patients at eight rural hospitals in the Midwestern United States during prospective prescription order review/entry from 2008 to 2011 were extracted from RxFusion® database (a home-grown system, i.e., internally developed program at The Nebraska Medical Center (TNMC) for capturing remote pharmacist-documented intervention data). The study authors conceptualized an analytical framework, mapping the 37 classes of remote pharmacist interventions to three broader-level definitions: (a) intervention, eight categories (interaction/potential interaction, contraindication, adverse effects, anticoagulation monitoring, drug product selection, drug regimen, summary, and recommendation), (b) patient medication management, two categories (therapy review and action), and (c) health system-centered medication use process, four categories (prescribing, transcribing and documenting, administering, and monitoring). Frequencies of intervention levels were compared by year, hospital, remote pharmacist, and hospital pharmacy status (with a remote pharmacist and on-site pharmacist or with a remote pharmacist only) using chi-squared test and univariate logistic regression analyses, as appropriate. Results: For 450,000 prescription orders 19,222 remote pharmacist interventions were documented. Frequency of interventions significantly increased each year (36% in 2009, 55% in 2010, and 7% in 2011) versus the baseline year (2008, 3%) when service started. The frequency of interventions also differed significantly across the eight hospitals and 16 remote pharmacists for the three defined intervention levels and categories. Remote pharmacist interventions at hospitals with an on-site and remote pharmacist (n=12,141) versus those with a remote pharmacist alone (n=7,081) were significantly more likely to be (1) patient-centered, (2) related to “actionable” medication management recommendations (unadjusted odds ratio [OR]=1.12), and (3) related to the “transcribing” (OR=1.47) and “prescribing” (OR=1.40) steps of the health system-centered medication use process level (all p<0.01). Conclusions: This is one of the first studies to demonstrate the patient- and health system-centered nature of pharmaceutical care delivered via a telepharmacy service model by evaluating documented remote pharmacist interventions with an analytical framework. PMID:24611489

Osiris-REx Spacecraft Current Status and Forward Plans

NASA Technical Reports Server (NTRS)

Messenger, Scott; Lauretta, Dante S.; Connolly, Harold C., Jr.

2017-01-01

The NASA New Frontiers OSIRIS-REx spacecraft executed a flawless launch on September 8, 2016 to begin its 23-month journey to near-Earth asteroid (101955). The primary objective of the OSIRIS-REx mission is to collect and return to Earth a pristine sample of regolith from the asteroid surface. The sampling event will occur after a two-year period of remote sensing that will ensure a high probability of successful sampling of a region on the asteroid surface having high science value and within well-defined geological context. The OSIRIS-REx instrument payload includes three high-resolution cameras (OCAMS), a visible and near-infrared spectrometer (OVIRS), a thermal imaging spectrometer (OTES), an X-ray imaging spectrometer (REXIS), and a laser altimeter (OLA). As the spacecraft follows its nominal outbound-cruise trajectory, the propulsion, power, communications, and science instruments have undergone basic functional tests, with no major issues. Outbound cruise science investigations include a search for Earth Trojan asteroids as the spacecraft approaches the Sun-Earth L4 Lagrangian point in February 2017. Additional instrument checkouts and calibrations will be carried out during the Earth gravity assist maneuver in September 2017. During the Earth-moon flyby, visual and spectral images will be acquired to validate instrument command sequences planned for Bennu remote sensing. The asteroid Bennu remote sensing campaign will yield high resolution maps of the temperature and thermal inertia, distributions of major minerals and concentrations of organic matter across the asteroid surface. A high resolution 3d shape model including local surface slopes and a high-resolution gravity field will also be determined. Together, these data will be used to generate four separate maps that will be used to select the sampling site(s). The Safety map will identify hazardous and safe operational regions on the asteroid surface. The Deliverability map will quantify the accuracy with which the navigation team can deliver the spacecraft to and from specific sites on the asteroid surface. The Sampleability map quantifies the regolith properties, providing an estimation of how much material would be sampled at different points on the surface. The final Science Value map synthesizes the chemical, mineralogical, and geological, observations to identify the areas of the asteroid surface with the highest science value. Here, priority is given to organic, water-rich regions that have been minimally altered by surface processes. Asteroid surface samples will be acquired with a touch-and-go sample acquisition system (TAGSAM) that uses high purity pressurized N2 gas to mobilize regolith into a stainless steel canister. Although the mission requirement is to collect at least 60 g of material, tests of the TAGSAM routinely exceeded 300 g of simulant in micro-gravity tests. After acquiring the sample, the spacecraft will depart Bennu in 2021 to begin its return journey, with the sample return capsule landing at the Utah Test and Training Range on September 23, 2023. The OSIRIS-REx science team will carry out a series of detailed chemical, mineralogical, isotopic, and spectral studies that will be used to determine the origin and history of Bennu and to relate high spatial resolution sample studies to the global geological context from remote sensing. The outline of the sample analysis plan is described in a companion abstract.

Using the Opposition Effect in Remotely Sensed Data to Assist in the Retrieval of Bulk Density

NASA Astrophysics Data System (ADS)

Ambeau, Brittany L.

Bulk density is an important geophysical property that impacts the mobility of military vehicles and personnel. Accurate retrieval of bulk density from remotely sensed data is, therefore, needed to estimate the mobility on "off-road" terrain. For a particulate surface, the functional form of the opposition effect can provide valuable information about composition and structure. In this research, we examine the relationship between bulk density and angular width of the opposition effect for a controlled set of laboratory experiments. Given a sample with a known bulk density, we collect reflectance measurements on a spherical grid for various illumination and view geometries -- increasing the amount of reflectance measurements collected at small phase angles near the opposition direction. Bulk densities are varied using a custom-made pluviation device, samples are measured using the Goniometer of the Rochester Institute of Technology-Two (GRIT-T), and observations are fit to the Hapke model using a grid-search method. The method that is selected allows for the direct estimation of five parameters: the single-scattering albedo, the amplitude of the opposition effect, the angular width of the opposition effect, and the two parameters that describe the single-particle phase function. As a test of the Hapke model, the retrieved bulk densities are compared to the known bulk densities. Results show that with an increase in the availability of multi-angular reflectance measurements, the prospects for retrieving the spatial distribution of bulk density from satellite and airborne sensors are imminent.

Development of a regional ocean color algorithm using field- and satellite-derived datasets: Long Bay, South Carolina

NASA Astrophysics Data System (ADS)

Ryan, Kimberly Susan

Coastal and inland waters represent a diverse set of resources that support natural habitat and provide numerous ecosystem services to the human population. Conventional techniques to monitor water quality using in situ sensors and laboratory analysis of water samples can be very time- and cost-intensive. Alternatively, remote sensing techniques offer better spatial coverage and temporal resolution to accurately characterize the dynamic and unique water quality parameters. However, bio and geo-optical models are required that relate the remotely sensed spectral data with color producing agents (CPAs) that define the water quality. These CPAs include chlorophyll-a, suspended sediments, and colored-dissolved organic matter. Developing these models may be challenging for coastal environments such as Long Bay, South Carolina, due to the presence of multiple optically interfering CPAs. In this work, a regionally tiered ocean color model was developed using band ratio techniques to specifically predict the variability of chlorophyll-a concentrations in the turbid Long Bay waters. This model produced higher accuracy results (r-squared = 0.62; RMSE = 0.87 micrograms per liter) compared to the existing models, which gave a highest r-squared value of 0.58 and RMSE = 0.99 micrograms per liter. To further enhance the retrievals of chlorophyll-a in these optically complex waters, a novel multivariate-based approach was developed using current generation hyperspectral data. This approach uses a partial least-squares regression model to identify wavelengths that are more sensitive to chlorophyll-a relative to other associated CPAs. This model was able to explain 80% of the observed chlorophyll-a variability in Long Bay with RMSE = 2.03 micrograms per liter. This approach capitalizes on the spectral advantage gained from hyperspectral sensors, thus providing a more robust predicting model. This enhanced mode of water quality monitoring in marine environments will provide insight to point-sources and problem areas that may contribute to a decline in water quality. Moreover, remote sensing applications such as this can be used as a tool for coastal and fisheries managers with regard to recreation, regulation, economic and public health purposes.

Remote object authentication: confidence model, cryptosystem and protocol

NASA Astrophysics Data System (ADS)

Lancrenon, Jean; Gillard, Roland; Fournel, Thierry

2009-04-01

This paper follows a paper by Bringer et al.3 to adapt a security model and protocol used for remote biometric authentication to the case of remote morphometric object authentication. We use a different type of encryption technique that requires smaller key sizes and has a built-in mechanism to help control the integrity of the messages received by the server. We also describe the optical technology used to extract the morphometric templates.

Estimating modal abundances from the spectra of natural and laboratory pyroxene mixtures using the modified Gaussian model

NASA Technical Reports Server (NTRS)

Sunshine, Jessica M.; Pieters, Carle M.

1993-01-01

The modified Gaussian model (MGM) is used to explore spectra of samples containing multiple pyroxene components as a function of modal abundance. The MGM allows spectra to be analyzed directly, without the use of actual or assumed end-member spectra and therefore holds great promise for remote applications. A series of mass fraction mixtures created from several different particle size fractions are analyzed with the MGM to quantify the properties of pyroxene mixtures as a function of both modal abundance and grain size. Band centers, band widths, and relative band strengths of absorptions from individual pyroxenes in mixture spectra are found to be largely independent of particle size. Spectral properties of both zoned and exsolved pyroxene components are resolved in exsolved samples using the MGM, and modal abundances are accurately estimated to within 5-10 percent without predetermined knowledge of the end-member spectra.

MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification

NASA Astrophysics Data System (ADS)

Lin, Daoyu; Fu, Kun; Wang, Yang; Xu, Guangluan; Sun, Xian

2017-11-01

With the development of deep learning, supervised learning has frequently been adopted to classify remotely sensed images using convolutional networks (CNNs). However, due to the limited amount of labeled data available, supervised learning is often difficult to carry out. Therefore, we proposed an unsupervised model called multiple-layer feature-matching generative adversarial networks (MARTA GANs) to learn a representation using only unlabeled data. MARTA GANs consists of both a generative model $G$ and a discriminative model $D$. We treat $D$ as a feature extractor. To fit the complex properties of remote sensing data, we use a fusion layer to merge the mid-level and global features. $G$ can produce numerous images that are similar to the training data; therefore, $D$ can learn better representations of remotely sensed images using the training data provided by $G$. The classification results on two widely used remote sensing image databases show that the proposed method significantly improves the classification performance compared with other state-of-the-art methods.

High-Resolution Remote Sensing Image Building Extraction Based on Markov Model

NASA Astrophysics Data System (ADS)

Zhao, W.; Yan, L.; Chang, Y.; Gong, L.

2018-04-01

With the increase of resolution, remote sensing images have the characteristics of increased information load, increased noise, more complex feature geometry and texture information, which makes the extraction of building information more difficult. To solve this problem, this paper designs a high resolution remote sensing image building extraction method based on Markov model. This method introduces Contourlet domain map clustering and Markov model, captures and enhances the contour and texture information of high-resolution remote sensing image features in multiple directions, and further designs the spectral feature index that can characterize "pseudo-buildings" in the building area. Through the multi-scale segmentation and extraction of image features, the fine extraction from the building area to the building is realized. Experiments show that this method can restrain the noise of high-resolution remote sensing images, reduce the interference of non-target ground texture information, and remove the shadow, vegetation and other pseudo-building information, compared with the traditional pixel-level image information extraction, better performance in building extraction precision, accuracy and completeness.

Assessing the effectiveness of Landsat 8 chlorophyll a retrieval algorithms for regional freshwater monitoring.

PubMed

Boucher, Jonah; Weathers, Kathleen C; Norouzi, Hamid; Steele, Bethel

2018-06-01

Predicting algal blooms has become a priority for scientists, municipalities, businesses, and citizens. Remote sensing offers solutions to the spatial and temporal challenges facing existing lake research and monitoring programs that rely primarily on high-investment, in situ measurements. Techniques to remotely measure chlorophyll a (chl a) as a proxy for algal biomass have been limited to specific large water bodies in particular seasons and narrow chl a ranges. Thus, a first step toward prediction of algal blooms is generating regionally robust algorithms using in situ and remote sensing data. This study explores the relationship between in-lake measured chl a data from Maine and New Hampshire, USA lakes and remotely sensed chl a retrieval algorithm outputs. Landsat 8 images were obtained and then processed after required atmospheric and radiometric corrections. Six previously developed algorithms were tested on a regional scale on 11 scenes from 2013 to 2015 covering 192 lakes. The best performing algorithm across data from both states had a 0.16 correlation coefficient (R 2 ) and P ≤ 0.05 when Landsat 8 images within 5 d, and improved to R 2 of 0.25 when data from Maine only were used. The strength of the correlation varied with the specificity of the time window in relation to the in-situ sampling date, explaining up to 27% of the variation in the data across several scenes. Two previously published algorithms using Landsat 8's Bands 1-4 were best correlated with chl a, and for particular late-summer scenes, they accounted for up to 69% of the variation in in-situ measurements. A sensitivity analysis revealed that a longer time difference between in situ measurements and the satellite image increased uncertainty in the models, and an effect of the time of year on several indices was demonstrated. A regional model based on the best performing remote sensing algorithm was developed and was validated using independent in situ measurements and satellite images. These results suggest that, despite challenges including seasonal effects and low chl a thresholds, remote sensing could be an effective and accessible regional-scale tool for chl a monitoring programs in lakes. © 2018 The Authors. Ecological Applications published by Wiley Periodicals, Inc. on behalf of Ecological Society of America.

A Discrete Fracture Network Model with Stress-Driven Nucleation and Growth

NASA Astrophysics Data System (ADS)

Lavoine, E.; Darcel, C.; Munier, R.; Davy, P.

2017-12-01

The realism of Discrete Fracture Network (DFN) models, beyond the bulk statistical properties, relies on the spatial organization of fractures, which is not issued by purely stochastic DFN models. The realism can be improved by injecting prior information in DFN from a better knowledge of the geological fracturing processes. We first develop a model using simple kinematic rules for mimicking the growth of fractures from nucleation to arrest, in order to evaluate the consequences of the DFN structure on the network connectivity and flow properties. The model generates fracture networks with power-law scaling distributions and a percentage of T-intersections that are consistent with field observations. Nevertheless, a larger complexity relying on the spatial variability of natural fractures positions cannot be explained by the random nucleation process. We propose to introduce a stress-driven nucleation in the timewise process of this kinematic model to study the correlations between nucleation, growth and existing fracture patterns. The method uses the stress field generated by existing fractures and remote stress as an input for a Monte-Carlo sampling of nuclei centers at each time step. Networks so generated are found to have correlations over a large range of scales, with a correlation dimension that varies with time and with the function that relates the nucleation probability to stress. A sensibility analysis of input parameters has been performed in 3D to quantify the influence of fractures and remote stress field orientations.

Discrimination of soil hydraulic properties by combined thermal infrared and microwave remote sensing

NASA Technical Reports Server (NTRS)

Vandegriend, A. A.; Oneill, P. E.

1986-01-01

Using the De Vries models for thermal conductivity and heat capacity, thermal inertia was determined as a function of soil moisture for 12 classes of soil types ranging from sand to clay. A coupled heat and moisture balance model was used to describe the thermal behavior of the top soil, while microwave remote sensing was used to estimate the soil moisture content of the same top soil. Soil hydraulic parameters are found to be very highly correlated with the combination of soil moisture content and thermal inertia at the same moisture content. Therefore, a remotely sensed estimate of the thermal behavior of the soil from diurnal soil temperature observations and an independent remotely sensed estimate of soil moisture content gives the possibility of estimating soil hydraulic properties by remote sensing.

The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE)

PubMed Central

Tian, Xin; Li, Zengyuan; Chen, Erxue; Liu, Qinhuo; Yan, Guangjian; Wang, Jindi; Niu, Zheng; Zhao, Shaojie; Li, Xin; Pang, Yong; Su, Zhongbo; van der Tol, Christiaan; Liu, Qingwang; Wu, Chaoyang; Xiao, Qing; Yang, Le; Mu, Xihan; Bo, Yanchen; Qu, Yonghua; Zhou, Hongmin; Gao, Shuai; Chai, Linna; Huang, Huaguo; Fan, Wenjie; Li, Shihua; Bai, Junhua; Jiang, Lingmei; Zhou, Ji

2015-01-01

The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE) comprises a network of remote sensing experiments designed to enhance the dynamic analysis and modeling of remotely sensed information for complex land surfaces. Two types of experimental campaigns were established under the framework of COMPLICATE. The first was designed for continuous and elaborate experiments. The experimental strategy helps enhance our understanding of the radiative and scattering mechanisms of soil and vegetation and modeling of remotely sensed information for complex land surfaces. To validate the methodologies and models for dynamic analyses of remote sensing for complex land surfaces, the second campaign consisted of simultaneous satellite-borne, airborne, and ground-based experiments. During field campaigns, several continuous and intensive observations were obtained. Measurements were undertaken to answer key scientific issues, as follows: 1) Determine the characteristics of spatial heterogeneity and the radiative and scattering mechanisms of remote sensing on complex land surfaces. 2) Determine the mechanisms of spatial and temporal scale extensions for remote sensing on complex land surfaces. 3) Determine synergist inversion mechanisms for soil and vegetation parameters using multi-mode remote sensing on complex land surfaces. Here, we introduce the background, the objectives, the experimental designs, the observations and measurements, and the overall advances of COMPLICATE. As a result of the implementation of COMLICATE and for the next several years, we expect to contribute to quantitative remote sensing science and Earth observation techniques. PMID:26332035

REMOTE RAMAN SPECTROSCOPY OF VARIOUS MIXED AND COMPOSITE MINERAL PHASES AT 7.2 m DISTANCE

NASA Technical Reports Server (NTRS)

Sharma, S. K.; Misra, A. K.; Ismail, Syed; Singh, U. N.

2006-01-01

Remote Raman [e.g.,1-5] and micro-Raman spectroscopy [e.g., 6-10] are being evaluated on geological samples for their potential applications on Mars rover or lander. The Raman lines of minerals are sharp and distinct. The Raman finger-prints of minerals do not shift appreciably but remain distinct even in sub-micron grains and, therefore, can be used for mineral identification in fine-grained rocks [e.g., 4,7]. In this work we have evaluated the capability of a directly coupled remote Raman system (co-axial configuration) for distinguishing the mineralogy of multiple crystals in the exciting laser beam. We have measured the Raman spectra of minerals in the near vicinity of each other and excited with a laser beam (e.g. -quartz (Qz) and K-feldspar (Feld) plates, each 5 mm thick). The spectra of composite transparent mineral plates of 5 mm thickness of -quartz and gypsum over calcite crystal were measured with the composite samples perpendicular to the exciting laser beam. The measurements of remote Raman spectra of various bulk minerals, and mixed and composite minerals with our portable UH remote Raman system were carried out at the Langley Research Center in a fully illuminated laboratory.

Regional-scale estimates of surface moisture availability and thermal inertia using remote thermal measurements

NASA Technical Reports Server (NTRS)

Carlson, T. N.

1986-01-01

A review is presented of numerical models which were developed to interpret thermal IR data and to identify the governing parameters and surface energy fluxes recorded in the images. Analytic, predictive, diagnostic and empirical models are described. The limitations of each type of modeling approach are explored in terms of the error sources and inherent constraints due to theoretical or measurement limitations. Sample results of regional-scale soil moisture or evaporation patterns derived from the Heat Capacity Mapping Mission and GOES satellite data through application of the predictive model devised by Carlson (1981) are discussed. The analysis indicates that pattern recognition will probably be highest when data are collected over flat, arid, sparsely vegetated terrain. The soil moisture data then obtained may be accurate to within 10-20 percent.

Objected-oriented remote sensing image classification method based on geographic ontology model

NASA Astrophysics Data System (ADS)

Chu, Z.; Liu, Z. J.; Gu, H. Y.

2016-11-01

Nowadays, with the development of high resolution remote sensing image and the wide application of laser point cloud data, proceeding objected-oriented remote sensing classification based on the characteristic knowledge of multi-source spatial data has been an important trend on the field of remote sensing image classification, which gradually replaced the traditional method through improving algorithm to optimize image classification results. For this purpose, the paper puts forward a remote sensing image classification method that uses the he characteristic knowledge of multi-source spatial data to build the geographic ontology semantic network model, and carries out the objected-oriented classification experiment to implement urban features classification, the experiment uses protégé software which is developed by Stanford University in the United States, and intelligent image analysis software—eCognition software as the experiment platform, uses hyperspectral image and Lidar data that is obtained through flight in DaFeng City of JiangSu as the main data source, first of all, the experiment uses hyperspectral image to obtain feature knowledge of remote sensing image and related special index, the second, the experiment uses Lidar data to generate nDSM(Normalized DSM, Normalized Digital Surface Model),obtaining elevation information, the last, the experiment bases image feature knowledge, special index and elevation information to build the geographic ontology semantic network model that implement urban features classification, the experiment results show that, this method is significantly higher than the traditional classification algorithm on classification accuracy, especially it performs more evidently on the respect of building classification. The method not only considers the advantage of multi-source spatial data, for example, remote sensing image, Lidar data and so on, but also realizes multi-source spatial data knowledge integration and application of the knowledge to the field of remote sensing image classification, which provides an effective way for objected-oriented remote sensing image classification in the future.

Spatial Irrigation Management Using Remote Sensing Water Balance Modeling and Soil Water Content Monitoring

NASA Astrophysics Data System (ADS)

Barker, J. Burdette

Spatially informed irrigation management may improve the optimal use of water resources. Sub-field scale water balance modeling and measurement were studied in the context of irrigation management. A spatial remote-sensing-based evapotranspiration and soil water balance model was modified and validated for use in real-time irrigation management. The modeled ET compared well with eddy covariance data from eastern Nebraska. Placement and quantity of sub-field scale soil water content measurement locations was also studied. Variance reduction factor and temporal stability were used to analyze soil water content data from an eastern Nebraska field. No consistent predictor of soil water temporal stability patterns was identified. At least three monitoring locations were needed per irrigation management zone to adequately quantify the mean soil water content. The remote-sensing-based water balance model was used to manage irrigation in a field experiment. The research included an eastern Nebraska field in 2015 and 2016 and a western Nebraska field in 2016 for a total of 210 plot-years. The response of maize and soybean to irrigation using variations of the model were compared with responses from treatments using soil water content measurement and a rainfed treatment. The remote-sensing-based treatment prescribed more irrigation than the other treatments in all cases. Excessive modeled soil evaporation and insufficient drainage times were suspected causes of the model drift. Modifying evaporation and drainage reduced modeled soil water depletion error. None of the included response variables were significantly different between treatments in western Nebraska. In eastern Nebraska, treatment differences for maize and soybean included evapotranspiration and a combined variable including evapotranspiration and deep percolation. Both variables were greatest for the remote-sensing model when differences were found to be statistically significant. Differences in maize yield in 2015 were attributed to random error. Soybean yield was lowest for the remote-sensing-based treatment and greatest for rainfed, possibly because of overwatering and lodging. The model performed well considering that it did not include soil water content measurements during the season. Future work should improve the soil evaporation and drainage formulations, because of excessive precipitation and include aerial remote sensing imagery and soil water content measurement as model inputs.

A Remote Sensing Image Fusion Method based on adaptive dictionary learning

NASA Astrophysics Data System (ADS)

He, Tongdi; Che, Zongxi

2018-01-01

This paper discusses using a remote sensing fusion method, based on' adaptive sparse representation (ASP)', to provide improved spectral information, reduce data redundancy and decrease system complexity. First, the training sample set is formed by taking random blocks from the images to be fused, the dictionary is then constructed using the training samples, and the remaining terms are clustered to obtain the complete dictionary by iterated processing at each step. Second, the self-adaptive weighted coefficient rule of regional energy is used to select the feature fusion coefficients and complete the reconstruction of the image blocks. Finally, the reconstructed image blocks are rearranged and an average is taken to obtain the final fused images. Experimental results show that the proposed method is superior to other traditional remote sensing image fusion methods in both spectral information preservation and spatial resolution.

Grid workflow validation using ontology-based tacit knowledge: A case study for quantitative remote sensing applications

NASA Astrophysics Data System (ADS)

Liu, Jia; Liu, Longli; Xue, Yong; Dong, Jing; Hu, Yingcui; Hill, Richard; Guang, Jie; Li, Chi

2017-01-01

Workflow for remote sensing quantitative retrieval is the ;bridge; between Grid services and Grid-enabled application of remote sensing quantitative retrieval. Workflow averts low-level implementation details of the Grid and hence enables users to focus on higher levels of application. The workflow for remote sensing quantitative retrieval plays an important role in remote sensing Grid and Cloud computing services, which can support the modelling, construction and implementation of large-scale complicated applications of remote sensing science. The validation of workflow is important in order to support the large-scale sophisticated scientific computation processes with enhanced performance and to minimize potential waste of time and resources. To research the semantic correctness of user-defined workflows, in this paper, we propose a workflow validation method based on tacit knowledge research in the remote sensing domain. We first discuss the remote sensing model and metadata. Through detailed analysis, we then discuss the method of extracting the domain tacit knowledge and expressing the knowledge with ontology. Additionally, we construct the domain ontology with Protégé. Through our experimental study, we verify the validity of this method in two ways, namely data source consistency error validation and parameters matching error validation.

Leveraging this Golden Age of Remote Sensing and Modeling of Terrestrial Hydrology to Understand Water Cycling in the Water Availability Grand Challenge for North America

NASA Astrophysics Data System (ADS)

Painter, T. H.; Famiglietti, J. S.; Stephens, G. L.

2016-12-01

We live in a time of increasing strains on our global fresh water availability due to increasing population, warming climate, changes in precipitation, and extensive depletion of groundwater supplies. At the same time, we have seen enormous growth in capabilities to remotely sense the regional to global water cycle and model complex systems with physically based frameworks. The GEWEX Water Availability Grand Challenge for North America is poised to leverage this convergence of remote sensing and modeling capabilities to answer fundamental questions on the water cycle. In particular, we envision an experiment that targets the complex and resource-critical Western US from California to just into the Great Plains, constraining physically-based hydrologic modeling with the US and international remote sensing capabilities. In particular, the last decade has seen the implementation or soon-to-be launch of water cycle missions such as GRACE and GRACE-FO for groundwater, SMAP for soil moisture, GPM for precipitation, SWOT for terrestrial surface water, and the Airborne Snow Observatory for snowpack. With the advent of convection-resolving mesoscale climate and water cycle modeling (e.g. WRF, WRF-Hydro) and mesoscale models capable of quantitative assimilation of remotely sensed data (e.g. the JPL Western States Water Mission), we can now begin to test hypotheses on the nature and changes in the water cycle of the Western US from a physical standpoint. In turn, by fusing water cycle science, water management, and ecosystem management while addressing these hypotheses, this golden age of remote sensing and modeling can bring all fields into a markedly less uncertain state of present knowledge and decadal scale forecasts.

Contrasting seasonality in optical-biogeochemical properties of the Baltic Sea.

PubMed

Simis, Stefan G H; Ylöstalo, Pasi; Kallio, Kari Y; Spilling, Kristian; Kutser, Tiit

2017-01-01

Optical-biogeochemical relationships of particulate and dissolved organic matter are presented in support of remote sensing of the Baltic Sea pelagic. This system exhibits strong seasonality in phytoplankton community composition and wide gradients of chromophoric dissolved organic matter (CDOM), properties which are poorly handled by existing remote sensing algorithms. Absorption and scattering properties of particulate matter reflected the seasonality in biological (phytoplankton succession) and physical (thermal stratification) processes. Inherent optical properties showed much wider variability when normalized to the chlorophyll-a concentration compared to normalization to either total suspended matter dry weight or particulate organic carbon. The particle population had the largest optical variability in summer and was dominated by organic matter in both seasons. The geographic variability of CDOM and relationships with dissolved organic carbon (DOC) are also presented. CDOM dominated light absorption at blue wavelengths, contributing 81% (median) of the absorption by all water constituents at 400 nm and 63% at 442 nm. Consequentially, 90% of water-leaving radiance at 412 nm originated from a layer (z90) no deeper than approximately 1.0 m. With water increasingly attenuating light at longer wavelengths, a green peak in light penetration and reflectance is always present in these waters, with z90 up to 3.0-3.5 m depth, whereas z90 only exceeds 5 m at biomass < 5 mg Chla m-3. High absorption combined with a weakly scattering particle population (despite median phytoplankton biomass of 14.1 and 4.3 mg Chla m-3 in spring and summer samples, respectively), characterize this sea as a dark water body for which dedicated or exceptionally robust remote sensing techniques are required. Seasonal and regional optical-biogeochemical models, data distributions, and an extensive set of simulated remote-sensing reflectance spectra for testing of remote sensing algorithms are provided as supplementary data.

Remote Sensing Image Classification Applied to the First National Geographical Information Census of China

NASA Astrophysics Data System (ADS)

Yu, Xin; Wen, Zongyong; Zhu, Zhaorong; Xia, Qiang; Shun, Lan

2016-06-01

Image classification will still be a long way in the future, although it has gone almost half a century. In fact, researchers have gained many fruits in the image classification domain, but there is still a long distance between theory and practice. However, some new methods in the artificial intelligence domain will be absorbed into the image classification domain and draw on the strength of each to offset the weakness of the other, which will open up a new prospect. Usually, networks play the role of a high-level language, as is seen in Artificial Intelligence and statistics, because networks are used to build complex model from simple components. These years, Bayesian Networks, one of probabilistic networks, are a powerful data mining technique for handling uncertainty in complex domains. In this paper, we apply Tree Augmented Naive Bayesian Networks (TAN) to texture classification of High-resolution remote sensing images and put up a new method to construct the network topology structure in terms of training accuracy based on the training samples. Since 2013, China government has started the first national geographical information census project, which mainly interprets geographical information based on high-resolution remote sensing images. Therefore, this paper tries to apply Bayesian network to remote sensing image classification, in order to improve image interpretation in the first national geographical information census project. In the experiment, we choose some remote sensing images in Beijing. Experimental results demonstrate TAN outperform than Naive Bayesian Classifier (NBC) and Maximum Likelihood Classification Method (MLC) in the overall classification accuracy. In addition, the proposed method can reduce the workload of field workers and improve the work efficiency. Although it is time consuming, it will be an attractive and effective method for assisting office operation of image interpretation.

SAMPLING SYSTEM

DOEpatents

Hannaford, B.A.; Rosenberg, R.; Segaser, C.L.; Terry, C.L.

1961-01-17

An apparatus is given for the batch sampling of radioactive liquids such as slurries from a system by remote control, while providing shielding for protection of operating personnel from the harmful effects of radiation.

[Comparison of precision in retrieving soybean leaf area index based on multi-source remote sensing data].

PubMed

Gao, Lin; Li, Chang-chun; Wang, Bao-shan; Yang Gui-jun; Wang, Lei; Fu, Kui

2016-01-01

With the innovation of remote sensing technology, remote sensing data sources are more and more abundant. The main aim of this study was to analyze retrieval accuracy of soybean leaf area index (LAI) based on multi-source remote sensing data including ground hyperspectral, unmanned aerial vehicle (UAV) multispectral and the Gaofen-1 (GF-1) WFV data. Ratio vegetation index (RVI), normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), difference vegetation index (DVI), and triangle vegetation index (TVI) were used to establish LAI retrieval models, respectively. The models with the highest calibration accuracy were used in the validation. The capability of these three kinds of remote sensing data for LAI retrieval was assessed according to the estimation accuracy of models. The experimental results showed that the models based on the ground hyperspectral and UAV multispectral data got better estimation accuracy (R² was more than 0.69 and RMSE was less than 0.4 at 0.01 significance level), compared with the model based on WFV data. The RVI logarithmic model based on ground hyperspectral data was little superior to the NDVI linear model based on UAV multispectral data (The difference in E(A), R² and RMSE were 0.3%, 0.04 and 0.006, respectively). The models based on WFV data got the lowest estimation accuracy with R2 less than 0.30 and RMSE more than 0.70. The effects of sensor spectral response characteristics, sensor geometric location and spatial resolution on the soybean LAI retrieval were discussed. The results demonstrated that ground hyperspectral data were advantageous but not prominent over traditional multispectral data in soybean LAI retrieval. WFV imagery with 16 m spatial resolution could not meet the requirements of crop growth monitoring at field scale. Under the condition of ensuring the high precision in retrieving soybean LAI and working efficiently, the approach to acquiring agricultural information by UAV remote sensing could yet be regarded as an optimal plan. Therefore, in the case of more and more available remote sensing information sources, agricultural UAV remote sensing could become an important information resource for guiding field-scale crop management and provide more scientific and accurate information for precision agriculture research.

Validity of the remote food photography method against doubly labeled water among minority preschoolers

USDA-ARS?s Scientific Manuscript database

The aim of this study was to determine the validity of energy intake (EI) estimations made using the remote food photography method (RFPM) compared to the doubly labeled water (DLW) method in minority preschool children in a free-living environment. Seven days of food intake and spot urine samples...

Simulation of CIFF (Centralized IFF) remote control displays

NASA Astrophysics Data System (ADS)

Tucker, D. L.; Leibowitz, L. M.

1986-06-01

This report presents the software simulation of the Remote-Control-Display (RCS) proposed to be used in the Centralized IFF (CIFF) system. A description of the simulation programs along with simulated menu formats are presented. A sample listing of the simulation programs and a brief description of the program operation are also included.

Use of remote-sensing techniques to survey the physical habitat of large rivers

USGS Publications Warehouse

Edsall, Thomas A.; Behrendt, Thomas E.; Cholwek, Gary; Frey, Jeffery W.; Kennedy, Gregory W.; Smith, Stephen B.; Edsall, Thomas A.; Behrendt, Thomas E.; Cholwek, Gary; Frey, Jeffrey W.; Kennedy, Gregory W.; Smith, Stephen B.

1997-01-01

Remote-sensing techniques that can be used to quantitatively characterize the physical habitat in large rivers in the United States where traditional survey approaches typically used in small- and medium-sized streams and rivers would be ineffective or impossible to apply. The state-of-the-art remote-sensing technologies that we discuss here include side-scan sonar, RoxAnn, acoustic Doppler current profiler, remotely operated vehicles and camera systems, global positioning systems, and laser level survey systems. The use of these technologies will permit the collection of information needed to create computer visualizations and hard copy maps and generate quantitative databases that can be used in real-time mode in the field to characterize the physical habitat at a study location of interest and to guide the distribution of sampling effort needed to address other habitat-related study objectives. This report augments habitat sampling and characterization guidance provided by Meador et al. (1993) and is intended for use primarily by U.S. Geological Survey National Water Quality Assessment program managers and scientists who are documenting water quality in streams and rivers of the United States.

Smart Vest: wearable multi-parameter remote physiological monitoring system.

PubMed

Pandian, P S; Mohanavelu, K; Safeer, K P; Kotresh, T M; Shakunthala, D T; Gopal, Parvati; Padaki, V C

2008-05-01

The wearable physiological monitoring system is a washable shirt, which uses an array of sensors connected to a central processing unit with firmware for continuously monitoring physiological signals. The data collected can be correlated to produce an overall picture of the wearer's health. In this paper, we discuss the wearable physiological monitoring system called 'Smart Vest'. The Smart Vest consists of a comfortable to wear vest with sensors integrated for monitoring physiological parameters, wearable data acquisition and processing hardware and remote monitoring station. The wearable data acquisition system is designed using microcontroller and interfaced with wireless communication and global positioning system (GPS) modules. The physiological signals monitored are electrocardiogram (ECG), photoplethysmogram (PPG), body temperature, blood pressure, galvanic skin response (GSR) and heart rate. The acquired physiological signals are sampled at 250samples/s, digitized at 12-bit resolution and transmitted wireless to a remote physiological monitoring station along with the geo-location of the wearer. The paper describes a prototype Smart Vest system used for remote monitoring of physiological parameters and the clinical validation of the data are also presented.

Ocean experiments and remotely sensed images of chemically dispersed oil spills

NASA Technical Reports Server (NTRS)

Croswell, W. F.; Fedors, J. C.; Hoge, F. E.; Swift, R. N.; Johnson, J. C.

1983-01-01

A series of experiments was performed at sea where the effectiveness of dispersants applied from a helicopter was tested on fresh and weathered crude oils released from a surface research vessel. In conjunction with these experiments, remote sensing measurements using an array of airborne optical and microwave sensors were performed in order to aid in the interpretation of the dispersant effectiveness and to obtain quantitative images of oil on the sea under controlled conditions. Surface oil thickness and volume are inferred from airborne measurements using a dual-channel microwave imaging radiometer, aerial color photography, and an airborne oceanographic lidar. The remotely sensed measurements are compared with point sampled data obtained using a research vessel. The mass balance computations of surface versus subsurface oil volume using remotely sensed and point sampled data are consistent with each other and with the volumes of oil released. Data collected by the several techniques concur in indicating that, for the oils used and under the sea conditions encountered, the dispersant and application method are primarily useful when applied to fresh oil.

Remote sensing strategic exploration of large or superlarge gold ore deposits

NASA Astrophysics Data System (ADS)

Yan, Shouxun; Liu, Qingsheng; Wang, Hongmei; Wang, Zhigang; Liu, Suhong

1998-08-01

To prospect large or superlarge gold ore deposits, blending of remote sensing techniques and modern metallogenitic theories is one of the effective measures. The theory of metallogeny plays a director role before and during remote sensing technique applications. The remote sensing data with different platforms and different resolutions can be respectively applied to detect direct or indirect metallogenic information, and to identify the ore-controlling structure, especially, the ore-controlling structural assemblage, which, conversely, usually are the new conditions to study and to modify the metallogenic model, and to further develop the exploration model of large or superlarge ore deposits. Guidance by an academic idea of 'adjustment structure' which is the conceptual model of transverse structure, an obscured ore- controlling transverse structure has been identified on the refined TM imagery in the Hadamengou gold ore deposit, Setai Hyperspectral Geological Remote Sensing Testing Site (SHGRSTS), Wulashan mountains, Inner Mongolia, China. Meanwhile, The MAIS data has been applied to quickly identify the auriferous alteration rocks with Correspondence Analysis method and Spectral Angle Mapping (SAM) technique. The theoretical system and technical method of remote sensing strategic exploration of large or superlarge gold ore deposits have been demonstrated by the practices in the SHGRSTS.

Integrating remotely sensed land cover observations and a biogeochemical model for estimating forest ecosystem carbon dynamics

USGS Publications Warehouse

Liu, J.; Liu, S.; Loveland, Thomas R.; Tieszen, L.L.

2008-01-01

Land cover change is one of the key driving forces for ecosystem carbon (C) dynamics. We present an approach for using sequential remotely sensed land cover observations and a biogeochemical model to estimate contemporary and future ecosystem carbon trends. We applied the General Ensemble Biogeochemical Modelling System (GEMS) for the Laurentian Plains and Hills ecoregion in the northeastern United States for the period of 1975-2025. The land cover changes, especially forest stand-replacing events, were detected on 30 randomly located 10-km by 10-km sample blocks, and were assimilated by GEMS for biogeochemical simulations. In GEMS, each unique combination of major controlling variables (including land cover change history) forms a geo-referenced simulation unit. For a forest simulation unit, a Monte Carlo process is used to determine forest type, forest age, forest biomass, and soil C, based on the Forest Inventory and Analysis (FIA) data and the U.S. General Soil Map (STATSGO) data. Ensemble simulations are performed for each simulation unit to incorporate input data uncertainty. Results show that on average forests of the Laurentian Plains and Hills ecoregion have been sequestrating 4.2 Tg C (1 teragram = 1012 gram) per year, including 1.9 Tg C removed from the ecosystem as the consequences of land cover change. ?? 2008 Elsevier B.V.

Integrated Instrument Simulator Suites for Earth Science

NASA Technical Reports Server (NTRS)

Tanelli, Simone; Tao, Wei-Kuo; Matsui, Toshihisa; Hostetler, Chris; Hair, Johnathan; Butler, Carolyn; Kuo, Kwo-Sen; Niamsuwan, Noppasin; Johnson, Michael P.; Jacob, Joseph C.;

Scaling isotopic emissions and microbes across a permafrost thaw landscape

NASA Astrophysics Data System (ADS)

Varner, R. K.; Palace, M. W.; Saleska, S. R.; Bolduc, B.; Braswell, B. H., Jr.; Crill, P. M.; Chanton, J.; DelGreco, J.; Deng, J.; Frolking, S. E.; Herrick, C.; Hines, M. E.; Li, C.; McArthur, K. J.; McCalley, C. K.; Persson, A.; Roulet, N. T.; Torbick, N.; Tyson, G. W.; Rich, V. I.

2017-12-01

High latitude peatlands are a significant source of atmospheric methane. This source is spatially and temporally heterogeneous, resulting in a wide range of emission estimates for the atmospheric budget. Increasing atmospheric temperatures are causing degradation of underlying permafrost, creating changes in surface soil moisture, the surface and sub-surface hydrological patterns, vegetation and microbial communities, but the consequences to rates and magnitudes of methane production and emissions are poorly accounted for in global budgets. We combined field observations, multi-source remote sensing data and biogeochemical modeling to predict methane dynamics, including the fraction derived from hydrogenotrophic versus acetoclastic microbial methanogenesis across Stordalen mire, a heterogeneous discontinuous permafrost wetland located in northernmost Sweden. Using the field measurement validated Wetland-DNDC biogeochemical model, we estimated mire-wide CH4 and del13CH4 production and emissions for 2014 with input from field and unmanned aerial system (UAS) image derived vegetation maps, local climatology and water table from insitu and remotely sensed data. Model simulated methanogenic pathways correlate with sequence-based observations of methanogen community composition in samples collected from across the permafrost thaw landscape. This approach enables us to link below ground microbial community composition with emissions and indicates a potential for scaling across broad areas of the Arctic region.

Water environmental management with the aid of remote sensing and GIS technology

NASA Astrophysics Data System (ADS)

Chen, Xiaoling; Yuan, Zhongzhi; Li, Yok-Sheung; Song, Hong; Hou, Yingzi; Xu, Zhanhua; Liu, Honghua; Wai, Onyx W.

2005-01-01

Water environment is associated with many disciplinary fields including sciences and management which makes it difficult to study. Timely observation, data getting and analysis on water environment are very important for decision makers who play an important role to maintain the sustainable development. This study focused on developing a plateform of water environment management based on remote sensing and GIS technology, and its main target is to provide with necessary information on water environment through spatial analysis and visual display in a suitable way. The work especially focused on three points, and the first one is related to technical issues of spatial data organization and communication with a combination of GIS and statistical software. A data-related model was proposed to solve the data communication between the mentioned systems. The second one is spatio-temporal analysis based on remote sensing and GIS. Water quality parameters of suspended sediment concentration and BOD5 were specially analyzed in this case, and the results suggested an obvious influence of land source pollution quantitatively in a spatial domain. The third one is 3D visualization of surface feature based on RS and GIS technology. The Pearl River estuary and HongKong's coastal waters in the South China Sea were taken as a case in this study. The software ARCGIS was taken as a basic platform to develop a water environmental management system. The sampling data of water quality in 76 monitoring stations of coastal water bodies and remote sensed images were selected in this study.

Adult mortality in a low-density tree population using high-resolution remote sensing.

PubMed

Kellner, James R; Hubbell, Stephen P

2017-06-01

We developed a statistical framework to quantify mortality rates in canopy trees observed using time series from high-resolution remote sensing. By timing the acquisition of remote sensing data with synchronous annual flowering in the canopy tree species Handroanthus guayacan, we made 2,596 unique detections of 1,006 individual adult trees within 18,883 observation attempts on Barro Colorado Island, Panama (BCI) during an 11-yr period. There were 1,057 observation attempts that resulted in missing data due to cloud cover or incomplete spatial coverage. Using the fraction of 123 individuals from an independent field sample that were detected by satellite data (109 individuals, 88.6%), we estimate that the adult population for this species on BCI was 1,135 individuals. We used a Bayesian state-space model that explicitly accounted for the probability of tree detection and missing observations to compute an annual adult mortality rate of 0.2%·yr -1 (SE = 0.1, 95% CI = 0.06-0.45). An independent estimate of the adult mortality rate from 260 field-checked trees closely matched the landscape-scale estimate (0.33%·yr -1 , SE = 0.16, 95% CI = 0.12-0.74). Our proof-of-concept study shows that one can remotely estimate adult mortality rates for canopy tree species precisely in the presence of variable detection and missing observations. © 2017 by the Ecological Society of America.

Chemistry of groundwater discharge inferred from longitudinal river sampling

NASA Astrophysics Data System (ADS)

Batlle-Aguilar, J.; Harrington, G. A.; Leblanc, M.; Welch, C.; Cook, P. G.

2014-02-01

We present an approach for identifying groundwater discharge chemistry and quantifying spatially distributed groundwater discharge into rivers based on longitudinal synoptic sampling and flow gauging of a river. The method is demonstrated using a 450 km reach of a tropical river in Australia. Results obtained from sampling for environmental tracers, major ions, and selected trace element chemistry were used to calibrate a steady state one-dimensional advective transport model of tracer distribution along the river. The model closely reproduced river discharge and environmental tracer and chemistry composition along the study length. It provided a detailed longitudinal profile of groundwater inflow chemistry and discharge rates, revealing that regional fractured mudstones in the central part of the catchment contributed up to 40% of all groundwater discharge. Detailed analysis of model calibration errors and modeled/measured groundwater ion ratios elucidated that groundwater discharging in the top of the catchment is a mixture of local groundwater and bank storage return flow, making the method potentially useful to differentiate between local and regional sourced groundwater discharge. As the error in tracer concentration induced by a flow event applies equally to any conservative tracer, we show that major ion ratios can still be resolved with minimal error when river samples are collected during transient flow conditions. The ability of the method to infer groundwater inflow chemistry from longitudinal river sampling is particularly attractive in remote areas where access to groundwater is limited or not possible, and for identification of actual fluxes of salts and/or specific contaminant sources.

Quantification of terrestrial ecosystem carbon dynamics in the conterminous United States combining a process-based biogeochemical model and MODIS and AmeriFlux data

USDA-ARS?s Scientific Manuscript database

Satellite remote sensing provides continuous temporal and spatial information of terrestrial ecosystems. Using these remote sensing data and eddy flux measurements and biogeochemical models, such as the Terrestrial Ecosystem Model (TEM), should provide a more adequate quantification of carbon dynami...

Integration of Process Models and Remote Sensing for Estimating Productivity, Soil Moisture, and Energy Fluxes in a Tallgrass Prairie Ecosystem

EPA Science Inventory

We describe a research program aimed at integrating remotely sensed data with an ecosystem model (VELMA) and a soil-vegetation-atmosphere transfer (SVAT) model (SEBS) for generating spatially explicit, regional scale estimates of productivity (biomass) and energy\\mass exchanges i...

Research in the application of spectral data to crop identification and assessment, volume 2

NASA Technical Reports Server (NTRS)

Daughtry, C. S. T. (Principal Investigator); Hixson, M. M.; Bauer, M. E.

1980-01-01

The development of spectrometry crop development stage models is discussed with emphasis on models for corn and soybeans. One photothermal and four thermal meteorological models are evaluated. Spectral data were investigated as a source of information for crop yield models. Intercepted solar radiation and soil productivity are identified as factors related to yield which can be estimated from spectral data. Several techniques for machine classification of remotely sensed data for crop inventory were evaluated. Early season estimation, training procedures, the relationship of scene characteristics to classification performance, and full frame classification methods were studied. The optimal level for combining area and yield estimates of corn and soybeans is assessed utilizing current technology: digital analysis of LANDSAT MSS data on sample segments to provide area estimates and regression models to provide yield estimates.