Polarbrdf: A General Purpose Python Package for Visualization Quantitative Analysis of Multi-Angular Remote Sensing Measurements

NASA Technical Reports Server (NTRS)

Singh, Manoj K.; Gautam, Ritesh; Gatebe, Charles K.; Poudyal, Rajesh

2016-01-01

The Bidirectional Reflectance Distribution Function (BRDF) is a fundamental concept for characterizing the reflectance property of a surface, and helps in the analysis of remote sensing data from satellite, airborne and surface platforms. Multi-angular remote sensing measurements are required for the development and evaluation of BRDF models for improved characterization of surface properties. However, multi-angular data and the associated BRDF models are typically multidimensional involving multi-angular and multi-wavelength information. Effective visualization of such complex multidimensional measurements for different wavelength combinations is presently somewhat lacking in the literature, and could serve as a potentially useful research and teaching tool in aiding both interpretation and analysis of BRDF measurements. This article describes a newly developed software package in Python (PolarBRDF) to help visualize and analyze multi-angular data in polar and False Color Composite (FCC) forms. PolarBRDF also includes functionalities for computing important multi-angular reflectance/albedo parameters including spectral albedo, principal plane reflectance and spectral reflectance slope. Application of PolarBRDF is demonstrated using various case studies obtained from airborne multi-angular remote sensing measurements using NASA's Cloud Absorption Radiometer (CAR). Our visualization program also provides functionalities for untangling complex surface/atmosphere features embedded in pixel-based remote sensing measurements, such as the FCC imagery generation of BRDF measurements of grasslands in the presence of wild fire smoke and clouds. Furthermore, PolarBRDF also provides quantitative information of the angular distribution of scattered surface/atmosphere radiation, in the form of relevant BRDF variables such as sunglint, hotspot and scattering statistics.

Object-oriented recognition of high-resolution remote sensing image

NASA Astrophysics Data System (ADS)

Wang, Yongyan; Li, Haitao; Chen, Hong; Xu, Yuannan

2016-01-01

With the development of remote sensing imaging technology and the improvement of multi-source image's resolution in satellite visible light, multi-spectral and hyper spectral , the high resolution remote sensing image has been widely used in various fields, for example military field, surveying and mapping, geophysical prospecting, environment and so forth. In remote sensing image, the segmentation of ground targets, feature extraction and the technology of automatic recognition are the hotspot and difficulty in the research of modern information technology. This paper also presents an object-oriented remote sensing image scene classification method. The method is consist of vehicles typical objects classification generation, nonparametric density estimation theory, mean shift segmentation theory, multi-scale corner detection algorithm, local shape matching algorithm based on template. Remote sensing vehicles image classification software system is designed and implemented to meet the requirements .

Remote sensing as a source of land cover information utilized in the universal soil loss equation

NASA Technical Reports Server (NTRS)

Morris-Jones, D. R.; Morgan, K. M.; Kiefer, R. W.; Scarpace, F. L.

1979-01-01

In this study, methods for gathering the land use/land cover information required by the USLE were investigated with medium altitude, multi-date color and color infrared 70-mm positive transparencies using human and computer-based interpretation techniques. Successful results, which compare favorably with traditional field study methods, were obtained within the test site watershed with airphoto data sources and human airphoto interpretation techniques. Computer-based interpretation techniques were not capable of identifying soil conservation practices but were successful to varying degrees in gathering other types of desired land use/land cover information.

DAFNE: A Matlab toolbox for Bayesian multi-source remote sensing and ancillary data fusion, with application to flood mapping

NASA Astrophysics Data System (ADS)

D'Addabbo, Annarita; Refice, Alberto; Lovergine, Francesco P.; Pasquariello, Guido

2018-03-01

High-resolution, remotely sensed images of the Earth surface have been proven to be of help in producing detailed flood maps, thanks to their synoptic overview of the flooded area and frequent revisits. However, flood scenarios can be complex situations, requiring the integration of different data in order to provide accurate and robust flood information. Several processing approaches have been recently proposed to efficiently combine and integrate heterogeneous information sources. In this paper, we introduce DAFNE, a Matlab®-based, open source toolbox, conceived to produce flood maps from remotely sensed and other ancillary information, through a data fusion approach. DAFNE is based on Bayesian Networks, and is composed of several independent modules, each one performing a different task. Multi-temporal and multi-sensor data can be easily handled, with the possibility of following the evolution of an event through multi-temporal output flood maps. Each DAFNE module can be easily modified or upgraded to meet different user needs. The DAFNE suite is presented together with an example of its application.

Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters

PubMed Central

Zhu, Hongchun; Cai, Lijie; Liu, Haiying; Huang, Wei

2016-01-01

Multi-scale image segmentation and the selection of optimal segmentation parameters are the key processes in the object-oriented information extraction of high-resolution remote sensing images. The accuracy of remote sensing special subject information depends on this extraction. On the basis of WorldView-2 high-resolution data, the optimal segmentation parameters methodof object-oriented image segmentation and high-resolution image information extraction, the following processes were conducted in this study. Firstly, the best combination of the bands and weights was determined for the information extraction of high-resolution remote sensing image. An improved weighted mean-variance method was proposed andused to calculatethe optimal segmentation scale. Thereafter, the best shape factor parameter and compact factor parameters were computed with the use of the control variables and the combination of the heterogeneity and homogeneity indexes. Different types of image segmentation parameters were obtained according to the surface features. The high-resolution remote sensing images were multi-scale segmented with the optimal segmentation parameters. Ahierarchical network structure was established by setting the information extraction rules to achieve object-oriented information extraction. This study presents an effective and practical method that can explain expert input judgment by reproducible quantitative measurements. Furthermore the results of this procedure may be incorporated into a classification scheme. PMID:27362762

Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters.

PubMed

Zhu, Hongchun; Cai, Lijie; Liu, Haiying; Huang, Wei

2016-01-01

Multi-scale image segmentation and the selection of optimal segmentation parameters are the key processes in the object-oriented information extraction of high-resolution remote sensing images. The accuracy of remote sensing special subject information depends on this extraction. On the basis of WorldView-2 high-resolution data, the optimal segmentation parameters methodof object-oriented image segmentation and high-resolution image information extraction, the following processes were conducted in this study. Firstly, the best combination of the bands and weights was determined for the information extraction of high-resolution remote sensing image. An improved weighted mean-variance method was proposed andused to calculatethe optimal segmentation scale. Thereafter, the best shape factor parameter and compact factor parameters were computed with the use of the control variables and the combination of the heterogeneity and homogeneity indexes. Different types of image segmentation parameters were obtained according to the surface features. The high-resolution remote sensing images were multi-scale segmented with the optimal segmentation parameters. Ahierarchical network structure was established by setting the information extraction rules to achieve object-oriented information extraction. This study presents an effective and practical method that can explain expert input judgment by reproducible quantitative measurements. Furthermore the results of this procedure may be incorporated into a classification scheme.

Multisource geological data mining and its utilization of uranium resources exploration

NASA Astrophysics Data System (ADS)

Zhang, Jie-lin

2009-10-01

Nuclear energy as one of clear energy sources takes important role in economic development in CHINA, and according to the national long term development strategy, many more nuclear powers will be built in next few years, so it is a great challenge for uranium resources exploration. Research and practice on mineral exploration demonstrates that utilizing the modern Earth Observe System (EOS) technology and developing new multi-source geological data mining methods are effective approaches to uranium deposits prospecting. Based on data mining and knowledge discovery technology, this paper uses multi-source geological data to character electromagnetic spectral, geophysical and spatial information of uranium mineralization factors, and provides the technical support for uranium prospecting integrating with field remote sensing geological survey. Multi-source geological data used in this paper include satellite hyperspectral image (Hyperion), high spatial resolution remote sensing data, uranium geological information, airborne radiometric data, aeromagnetic and gravity data, and related data mining methods have been developed, such as data fusion of optical data and Radarsat image, information integration of remote sensing and geophysical data, and so on. Based on above approaches, the multi-geoscience information of uranium mineralization factors including complex polystage rock mass, mineralization controlling faults and hydrothermal alterations have been identified, the metallogenic potential of uranium has been evaluated, and some predicting areas have been located.

Evapotranspiration estimates derived using multi-platform remote sensing in a semiarid region

USDA-ARS?s Scientific Manuscript database

Evapotranspiration (ET) is a key component of the water balance, especially in arid and semiarid regions. The current study takes advantage of spatially-distributed, near real-time information provided by satellite remote sensing to develop a regional scale ET product derived from remotely-sensed ob...

Optimal Joint Remote State Preparation of Arbitrary Equatorial Multi-qudit States

NASA Astrophysics Data System (ADS)

Cai, Tao; Jiang, Min

2017-03-01

As an important communication technology, quantum information transmission plays an important role in the future network communication. It involves two kinds of transmission ways: quantum teleportation and remote state preparation. In this paper, we put forward a new scheme for optimal joint remote state preparation (JRSP) of an arbitrary equatorial two-qudit state with hybrid dimensions. Moreover, the receiver can reconstruct the target state with 100 % success probability in a deterministic manner via two spatially separated senders. Based on it, we can extend it to joint remote preparation of arbitrary equatorial multi-qudit states with hybrid dimensions using the same strategy.

Multi-resource data-based research on remote sensing monitoring over the green tide in the Yellow Sea

NASA Astrophysics Data System (ADS)

Gao, Zhiqiang; Xu, Fuxiang; Song, Debin; Zheng, Xiangyu; Chen, Maosi

2017-09-01

This paper conducted dynamic monitoring over the green tide (large green alga—Ulva prolifera) occurred in the Yellow Sea in 2014 to 2016 by the use of multi-source remote sensing data, including GF-1 WFV, HJ-1A/1B CCD, CBERS-04 WFI, Landsat-7 ETM+ and Landsta-8 OLI, and by the combination of VB-FAH (index of Virtual-Baseline Floating macroAlgae Height) with manual assisted interpretation based on remote sensing and geographic information system technologies. The result shows that unmanned aerial vehicle (UAV) and shipborne platform could accurately monitor the distribution of Ulva prolifera in small spaces, and therefore provide validation data for the result of remote sensing monitoring over Ulva prolifera. The result of this research can provide effective information support for the prevention and control of Ulva prolifera.

Secure distribution for high resolution remote sensing images

NASA Astrophysics Data System (ADS)

Liu, Jin; Sun, Jing; Xu, Zheng Q.

2010-09-01

The use of remote sensing images collected by space platforms is becoming more and more widespread. The increasing value of space data and its use in critical scenarios call for adoption of proper security measures to protect these data against unauthorized access and fraudulent use. In this paper, based on the characteristics of remote sensing image data and application requirements on secure distribution, a secure distribution method is proposed, including users and regions classification, hierarchical control and keys generation, and multi-level encryption based on regions. The combination of the three parts can make that the same remote sensing images after multi-level encryption processing are distributed to different permission users through multicast, but different permission users can obtain different degree information after decryption through their own decryption keys. It well meets user access control and security needs in the process of high resolution remote sensing image distribution. The experimental results prove the effectiveness of the proposed method which is suitable for practical use in the secure transmission of remote sensing images including confidential information over internet.

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.

Development of multi-mission satellite data systems at the German Remote Sensing Data Centre

NASA Astrophysics Data System (ADS)

Lotz-Iwen, H. J.; Markwitz, W.; Schreier, G.

1998-11-01

This paper focuses on conceptual aspects of the access to multi-mission remote sensing data by online catalogue and information systems. The system ISIS of the German Remote Sensing Data Centre is described as an example of a user interface to earth observation data. ISIS has been designed to support international scientific research as well as operational applications by offering online access to the database via public networks. It provides catalogue retrieval, visualisation and transfer of image data, and is integrated in international activities dedicated to catalogue and archive interoperability. Finally, an outlook is given on international projects dealing with access to remote sensing data in distributed archives.

Multi-terminal remote monitoring and warning system using Micro Air Vehicle for dangerous environment

NASA Astrophysics Data System (ADS)

Yu, Yanan; Wang, Xiaoxun; He, Chengcheng; Lai, Chenlong; Liu, Yuanchao

2015-11-01

For overcoming the problems such as remote operation and dangerous tasks, multi-terminal remote monitoring and warning system based on STC89C52 Micro Control Unit and wireless communication technique was proposed. The system with MCU as its core adopted multiple sets of sensor device to monitor environment parameters of different locations, such as temperature, humidity, smoke other harmful gas concentration. Data information collected was transmitted remotely by wireless transceiver module, and then multi-channel data parameter was processed and displayed through serial communication protocol between the module and PC. The results of system could be checked in the form of web pages within a local network which plays a wireless monitoring and warning role. In a remote operation, four-rotor micro air vehicle which fixed airborne data acquisition device was utilized as a middleware between collecting terminal and PC to increase monitoring scope. Whole test system has characteristics of simple construction, convenience, real time ability and high reliability, which could meet the requirements of actual use.

Sea-land segmentation for infrared remote sensing images based on superpixels and multi-scale features

NASA Astrophysics Data System (ADS)

Lei, Sen; Zou, Zhengxia; Liu, Dunge; Xia, Zhenghuan; Shi, Zhenwei

2018-06-01

Sea-land segmentation is a key step for the information processing of ocean remote sensing images. Traditional sea-land segmentation algorithms ignore the local similarity prior of sea and land, and thus fail in complex scenarios. In this paper, we propose a new sea-land segmentation method for infrared remote sensing images to tackle the problem based on superpixels and multi-scale features. Considering the connectivity and local similarity of sea or land, we interpret the sea-land segmentation task in view of superpixels rather than pixels, where similar pixels are clustered and the local similarity are explored. Moreover, the multi-scale features are elaborately designed, comprising of gray histogram and multi-scale total variation. Experimental results on infrared bands of Landsat-8 satellite images demonstrate that the proposed method can obtain more accurate and more robust sea-land segmentation results than the traditional algorithms.

An efficient biometric and password-based remote user authentication using smart card for Telecare Medical Information Systems in multi-server environment.

PubMed

Maitra, Tanmoy; Giri, Debasis

2014-12-01

The medical organizations have introduced Telecare Medical Information System (TMIS) to provide a reliable facility by which a patient who is unable to go to a doctor in critical or urgent period, can communicate to a doctor through a medical server via internet from home. An authentication mechanism is needed in TMIS to hide the secret information of both parties, namely a server and a patient. Recent research includes patient's biometric information as well as password to design a remote user authentication scheme that enhances the security level. In a single server environment, one server is responsible for providing services to all the authorized remote patients. However, the problem arises if a patient wishes to access several branch servers, he/she needs to register to the branch servers individually. In 2014, Chuang and Chen proposed an remote user authentication scheme for multi-server environment. In this paper, we have shown that in their scheme, an non-register adversary can successfully logged-in into the system as a valid patient. To resist the weaknesses, we have proposed an authentication scheme for TMIS in multi-server environment where the patients can register to a root telecare server called registration center (RC) in one time to get services from all the telecare branch servers through their registered smart card. Security analysis and comparison shows that our proposed scheme provides better security with low computational and communication cost.

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.

Capturing remote mixing due to internal tides using multi-scale modeling tool: SOMAR-LES

NASA Astrophysics Data System (ADS)

Santilli, Edward; Chalamalla, Vamsi; Scotti, Alberto; Sarkar, Sutanu

2016-11-01

Internal tides that are generated during the interaction of an oscillating barotropic tide with the bottom bathymetry dissipate only a fraction of their energy near the generation region. The rest is radiated away in the form of low- high-mode internal tides. These internal tides dissipate energy at remote locations when they interact with the upper ocean pycnocline, continental slope, and large scale eddies. Capturing the wide range of length and time scales involved during the life-cycle of internal tides is computationally very expensive. A recently developed multi-scale modeling tool called SOMAR-LES combines the adaptive grid refinement features of SOMAR with the turbulence modeling features of a Large Eddy Simulation (LES) to capture multi-scale processes at a reduced computational cost. Numerical simulations of internal tide generation at idealized bottom bathymetries are performed to demonstrate this multi-scale modeling technique. Although each of the remote mixing phenomena have been considered independently in previous studies, this work aims to capture remote mixing processes during the life cycle of an internal tide in more realistic settings, by allowing multi-level (coarse and fine) grids to co-exist and exchange information during the time stepping process.

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.

Remote sensing for detecting and mapping whitefly (Bemisia tabaci) infestations

USDA-ARS?s Scientific Manuscript database

Remote sensing technology has long been used for detecting insect infestations on agricultural crops. With recent advances in remote sensing sensors and other spatial information technologies such as Global Position Systems (GPS) and Geographic Information Systems (GIS), remote sensing is finding mo...

A Multi-Temporal Remote Sensing Approach to Freshwater Turtle Conservation

NASA Astrophysics Data System (ADS)

Mui, Amy B.

Freshwater turtles are a globally declining taxa, and estimates of population status are not available for many species. Primary causes of decline stem from widespread habitat loss and degradation, and obtaining spatially-explicit information on remaining habitat across a relevant spatial scale has proven challenging. The discipline of remote sensing science has been employed widely in studies of biodiversity conservation, but it has not been utilized as frequently for cryptic, and less vagile species such as turtles, despite their vulnerable status. The work presented in this thesis investigates how multi-temporal remote sensing imagery can contribute key information for building spatially-explicit and temporally dynamic models of habitat and connectivity for the threatened, Blanding's turtle (Emydoidea blandingii) in southern Ontario, Canada. I began with outlining a methodological approach for delineating freshwater wetlands from high spatial resolution remote sensing imagery, using a geographic object-based image analysis (GEOBIA) approach. This method was applied to three different landscapes in southern Ontario, and across two biologically relevant seasons during the active (non-hibernating) period of Blanding's turtles. Next, relevant environmental variables associated with turtle presence were extracted from remote sensing imagery, and a boosted regression tree model was developed to predict the probability of occurrence of this species. Finally, I analysed the movement potential for Blanding's turtles in a disturbed landscape using a combination of approaches. Results indicate that (1) a parsimonious GEOBIA approach to land cover mapping, incorporating texture, spectral indices, and topographic information can map heterogeneous land cover with high accuracy, (2) remote-sensing derived environmental variables can be used to build habitat models with strong predictive power, and (3) connectivity potential is best estimated using a variety of approaches, though accurate estimates across human-altered landscapes is challenging. Overall, this body of work supports the use of remote sensing imagery in species distribution models to strengthen the precision, and power of predictive models, and also draws attention to the need to consider a multi-temporal examination of species habitat requirements.

Atmospheric correction for remote sensing image based on multi-spectral information

NASA Astrophysics Data System (ADS)

Wang, Yu; He, Hongyan; Tan, Wei; Qi, Wenwen

2018-03-01

The light collected from remote sensors taken from space must transit through the Earth's atmosphere. All satellite images are affected at some level by lightwave scattering and absorption from aerosols, water vapor and particulates in the atmosphere. For generating high-quality scientific data, atmospheric correction is required to remove atmospheric effects and to convert digital number (DN) values to surface reflectance (SR). Every optical satellite in orbit observes the earth through the same atmosphere, but each satellite image is impacted differently because atmospheric conditions are constantly changing. A physics-based detailed radiative transfer model 6SV requires a lot of key ancillary information about the atmospheric conditions at the acquisition time. This paper investigates to achieve the simultaneous acquisition of atmospheric radiation parameters based on the multi-spectral information, in order to improve the estimates of surface reflectance through physics-based atmospheric correction. Ancillary information on the aerosol optical depth (AOD) and total water vapor (TWV) derived from the multi-spectral information based on specific spectral properties was used for the 6SV model. The experimentation was carried out on images of Sentinel-2, which carries a Multispectral Instrument (MSI), recording in 13 spectral bands, covering a wide range of wavelengths from 440 up to 2200 nm. The results suggest that per-pixel atmospheric correction through 6SV model, integrating AOD and TWV derived from multispectral information, is better suited for accurate analysis of satellite images and quantitative remote sensing application.

Multi-National Information Sharing -- Cross Domain Collaborative Information Environment (CDCIE) Solution. Revision 4

DTIC Science & Technology

2005-04-12

Hardware, Database, and Operating System independence using Java • Enterprise-class Architecture using Java2 Enterprise Edition 1.4 • Standards based...portal applications. Compliance with the Java Specification Request for Portlet APIs (JSR-168) (Portlet API) and Web Services for Remote Portals...authentication and authorization • Portal Standards using Java Specification Request for Portlet APIs (JSR-168) (Portlet API) and Web Services for Remote

Disaster Emergency Rapid Assessment Based on Remote Sensing and Background Data

NASA Astrophysics Data System (ADS)

Han, X.; Wu, J.

2018-04-01

The period from starting to the stable conditions is an important stage of disaster development. In addition to collecting and reporting information on disaster situations, remote sensing images by satellites and drones and monitoring results from disaster-stricken areas should be obtained. Fusion of multi-source background data such as population, geography and topography, and remote sensing monitoring information can be used in geographic information system analysis to quickly and objectively assess the disaster information. According to the characteristics of different hazards, the models and methods driven by the rapid assessment of mission requirements are tested and screened. Based on remote sensing images, the features of exposures quickly determine disaster-affected areas and intensity levels, and extract key disaster information about affected hospitals and schools as well as cultivated land and crops, and make decisions after emergency response with visual assessment results.

Assessing Structure and Condition of Temperate And Tropical Forests: Fusion of Terrestrial Lidar and Airborne Multi-Angle and Lidar Remote Sensing

NASA Astrophysics Data System (ADS)

Saenz, Edward J.

Forests provide vital ecosystem functions and services that maintain the integrity of our natural and human environment. Understanding the structural components of forests (extent, tree density, heights of multi-story canopies, biomass, etc.) provides necessary information to preserve ecosystem services. Increasingly, remote sensing resources have been used to map and monitor forests globally. However, traditional satellite and airborne multi-angle imagery only provide information about the top of the canopy and little about the forest structure and understory. In this research, we investigative the use of rapidly evolving lidar technology, and how the fusion of aerial and terrestrial lidar data can be utilized to better characterize forest stand information. We further apply a novel terrestrial lidar methodology to characterize a Hemlock Woolly Adelgid infestation in Harvard Forest, Massachusetts, and adapt a dynamic terrestrial lidar sampling scheme to identify key structural vegetation profiles of tropical rainforests in La Selva, Costa Rica.

A sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image

NASA Astrophysics Data System (ADS)

Li, Jing; Xie, Weixin; Pei, Jihong

2018-03-01

Sea-land segmentation is one of the key technologies of sea target detection in remote sensing images. At present, the existing algorithms have the problems of low accuracy, low universality and poor automatic performance. This paper puts forward a sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image removing island. Firstly, the coastline data is extracted and all of land area is labeled by using the geographic information in large-field remote sensing image. Secondly, three features (local entropy, local texture and local gradient mean) is extracted in the sea-land border area, and the three features combine a 3D feature vector. And then the MultiGaussian model is adopted to describe 3D feature vectors of sea background in the edge of the coastline. Based on this multi-gaussian sea background model, the sea pixels and land pixels near coastline are classified more precise. Finally, the coarse segmentation result and the fine segmentation result are fused to obtain the accurate sea-land segmentation. Comparing and analyzing the experimental results by subjective vision, it shows that the proposed method has high segmentation accuracy, wide applicability and strong anti-disturbance ability.

Robust and adaptive band-to-band image transform of UAS miniature multi-lens multispectral camera

NASA Astrophysics Data System (ADS)

Jhan, Jyun-Ping; Rau, Jiann-Yeou; Haala, Norbert

2018-03-01

Utilizing miniature multispectral (MS) or hyperspectral (HS) cameras by mounting them on an Unmanned Aerial System (UAS) has the benefits of convenience and flexibility to collect remote sensing imagery for precision agriculture, vegetation monitoring, and environment investigation applications. Most miniature MS cameras adopt a multi-lens structure to record discrete MS bands of visible and invisible information. The differences in lens distortion, mounting positions, and viewing angles among lenses mean that the acquired original MS images have significant band misregistration errors. We have developed a Robust and Adaptive Band-to-Band Image Transform (RABBIT) method for dealing with the band co-registration of various types of miniature multi-lens multispectral cameras (Mini-MSCs) to obtain band co-registered MS imagery for remote sensing applications. The RABBIT utilizes modified projective transformation (MPT) to transfer the multiple image geometry of a multi-lens imaging system to one sensor geometry, and combines this with a robust and adaptive correction (RAC) procedure to correct several systematic errors and to obtain sub-pixel accuracy. This study applies three state-of-the-art Mini-MSCs to evaluate the RABBIT method's performance, specifically the Tetracam Miniature Multiple Camera Array (MiniMCA), Micasense RedEdge, and Parrot Sequoia. Six MS datasets acquired at different target distances and dates, and locations are also applied to prove its reliability and applicability. Results prove that RABBIT is feasible for different types of Mini-MSCs with accurate, robust, and rapid image processing efficiency.

Mapping daily evapotranspiration at field scales over rainfed and irrigated agricultural areas using remote sensing data fusion

USDA-ARS?s Scientific Manuscript database

A continuous monitoring of daily evapotranspiration (ET) at field scale can be achieved by combining thermal infrared remote sensing data information from multiple satellite platforms. Here, an integrated approach to field scale ET mapping is described, combining multi-scale surface energy balance e...

Assessment and prediction of land ecological environment quality change based on remote sensing-a case study of the Dongting lake area in China

NASA Astrophysics Data System (ADS)

Hu, Wenmin; Wang, Zhongcheng; Li, Chunhua; Zhao, Jin; Li, Yi

2018-02-01

Multi-source remote sensing data is rarely used for the comprehensive assessment of land ecologic environment quality. In this study, a digital environmental model was proposed with the inversion algorithm of land and environmental factors based on the multi-source remote sensing data, and a comprehensive index (Ecoindex) was applied to reconstruct and predict the land environment quality of the Dongting Lake Area to assess the effect of human activities on the environment. The main finding was that with the decrease of Grade I and Grade II quality had a decreasing tendency in the lake area, mostly in suburbs and wetlands. Atmospheric water vapour, land use intensity, surface temperature, vegetation coverage, and soil water content were the main driving factors. The cause of degradation was the interference of multi-factor combinations, which led to positive and negative environmental agglomeration effects. Positive agglomeration, such as increased rainfall and vegetation coverage and reduced land use intensity, could increase environmental quality, while negative agglomeration resulted in the opposite. Therefore, reasonable ecological restoration measures should be beneficial to limit the negative effects and decreasing tendency, improve the land ecological environment quality and provide references for macroscopic planning by the government.

Resource analysis applications in Michigan. [NASA remote sensing

NASA Technical Reports Server (NTRS)

Schar, S. W.; Enslin, W. R.; Sattinger, I. J.; Robinson, J. G.; Hosford, K. R.; Fellows, R. S.; Raad, J. H.

1974-01-01

During the past two years, available NASA imagery has been applied to a broad spectrum of problems of concern to Michigan-based agencies. These demonstrations include the testing of remote sensing for the purposes of (1) highway corridor planning and impact assessments, (2) game management-area information bases, (3) multi-agency river basin planning, (4) timber resource management information systems, (5) agricultural land reservation policies, and (6) shoreline flooding damage assessment. In addition, cost accounting procedures have been developed for evaluating the relative costs of utilizing remote sensing in land cover and land use analysis data collection procedures.

Design criteria for a PC-based common user interface to remote information systems

NASA Technical Reports Server (NTRS)

Dominick, Wayne D. (Editor); Hall, Philip P.

1984-01-01

A set of design criteria are presented which will allow the implementation of an interface to multiple remote information systems on a microcomputer. The focus of the design description is on providing the user with the functionality required to retrieve, store and manipulate data residing in remote information systems through the utilization of a standardized interface system. The intent is to spare the user from learning the details of retrieval from specific systems while retaining the full capabilities of each system. The system design includes multi-level capabilities to enhance usability by a wide range of users and utilizes microcomputer graphics capabilities where applicable. A data collection subsystem for evaluation purposes is also described.

Practical applications of remote sensing technology

NASA Technical Reports Server (NTRS)

Whitmore, Roy A., Jr.

1990-01-01

Land managers increasingly are becoming dependent upon remote sensing and automated analysis techniques for information gathering and synthesis. Remote sensing and geographic information system (GIS) techniques provide quick and economical information gathering for large areas. The outputs of remote sensing classification and analysis are most effective when combined with a total natural resources data base within the capabilities of a computerized GIS. Some examples are presented of the successes, as well as the problems, in integrating remote sensing and geographic information systems. The need to exploit remotely sensed data and the potential that geographic information systems offer for managing and analyzing such data continues to grow. New microcomputers with vastly enlarged memory, multi-fold increases in operating speed and storage capacity that was previously available only on mainframe computers are a reality. Improved raster GIS software systems have been developed for these high performance microcomputers. Vector GIS systems previously reserved for mini and mainframe systems are available to operate on these enhanced microcomputers. One of the more exciting areas that is beginning to emerge is the integration of both raster and vector formats on a single computer screen. This technology will allow satellite imagery or digital aerial photography to be presented as a background to a vector display.

Dual use display systems for telerobotics

NASA Technical Reports Server (NTRS)

Massimino, Michael J.; Meschler, Michael F.; Rodriguez, Alberto A.

1994-01-01

This paper describes a telerobotics display system, the Multi-mode Manipulator Display System (MMDS), that has applications for a variety of remotely controlled tasks. Designed primarily to assist astronauts with the control of space robotics systems, the MMDS has applications for ground control of space robotics as well as for toxic waste cleanup, undersea, remotely operated vehicles, and other environments which require remote operations. The MMDS has three modes: (1) Manipulator Position Display (MPD) mode, (2) Joint Angle Display (JAD) mode, and (3) Sensory Substitution (SS) mode. These three modes are discussed in the paper.

An integrated approach for updating cadastral maps in Pakistan using satellite remote sensing data

NASA Astrophysics Data System (ADS)

Ali, Zahir; Tuladhar, Arbind; Zevenbergen, Jaap

2012-08-01

Updating cadastral information is crucial for recording land ownership and property division changes in a timely fashioned manner. In most cases, the existing cadastral maps do not provide up-to-date information on land parcel boundaries. Such a situation demands that all the cadastral data and parcel boundaries information in these maps to be updated in a timely fashion. The existing techniques for acquiring cadastral information are discipline-oriented based on different disciplines such as geodesy, surveying, and photogrammetry. All these techniques require a large number of manpower, time, and cost when they are carried out separately. There is a need to integrate these techniques for acquiring cadastral information to update the existing cadastral data and (re)produce cadastral maps in an efficient manner. To reduce the time and cost involved in cadastral data acquisition, this study develops an integrated approach by integrating global position system (GPS) data, remote sensing (RS) imagery, and existing cadastral maps. For this purpose, the panchromatic image with 0.6 m spatial resolution and the corresponding multi-spectral image with 2.4 m spatial resolution and 3 spectral bands from QuickBird satellite were used. A digital elevation model (DEM) was extracted from SPOT-5 stereopairs and some ground control points (GCPs) were also used for ortho-rectifying the QuickBird images. After ortho-rectifying these images and registering the multi-spectral image to the panchromatic image, fusion between them was attained to get good quality multi-spectral images of these two study areas with 0.6 m spatial resolution. Cadastral parcel boundaries were then identified on QuickBird images of the two study areas via visual interpretation using participatory-GIS (PGIS) technique. The regions of study are the urban and rural areas of Peshawar and Swabi districts in the Khyber Pakhtunkhwa province of Pakistan. The results are the creation of updated cadastral maps with a lot of cadastral information which can be used in updating the existing cadastral data with less time and cost.

Connecticut permanent long-term bridge monitoring network, volume 3 : monitoring of a multi-steel girder composite bridge - I-91 SB over the Mattabesset River in Cromwell (bridge #3078).

DOT National Transportation Integrated Search

2014-08-01

This report describes the instrumentation and data acquisition for a multi-girder, composite steel bridge in Connecticut. The : computer-based remote monitoring system was developed to collect information on the girder bending strains. The monitoring...

Farm Management Support on Cloud Computing Platform: A System for Cropland Monitoring Using Multi-Source Remotely Sensed Data

NASA Astrophysics Data System (ADS)

Coburn, C. A.; Qin, Y.; Zhang, J.; Staenz, K.

2015-12-01

Food security is one of the most pressing issues facing humankind. Recent estimates predict that over one billion people don't have enough food to meet their basic nutritional needs. The ability of remote sensing tools to monitor and model crop production and predict crop yield is essential for providing governments and farmers with vital information to ensure food security. Google Earth Engine (GEE) is a cloud computing platform, which integrates storage and processing algorithms for massive remotely sensed imagery and vector data sets. By providing the capabilities of storing and analyzing the data sets, it provides an ideal platform for the development of advanced analytic tools for extracting key variables used in regional and national food security systems. With the high performance computing and storing capabilities of GEE, a cloud-computing based system for near real-time crop land monitoring was developed using multi-source remotely sensed data over large areas. The system is able to process and visualize the MODIS time series NDVI profile in conjunction with Landsat 8 image segmentation for crop monitoring. With multi-temporal Landsat 8 imagery, the crop fields are extracted using the image segmentation algorithm developed by Baatz et al.[1]. The MODIS time series NDVI data are modeled by TIMESAT [2], a software package developed for analyzing time series of satellite data. The seasonality of MODIS time series data, for example, the start date of the growing season, length of growing season, and NDVI peak at a field-level are obtained for evaluating the crop-growth conditions. The system fuses MODIS time series NDVI data and Landsat 8 imagery to provide information of near real-time crop-growth conditions through the visualization of MODIS NDVI time series and comparison of multi-year NDVI profiles. Stakeholders, i.e., farmers and government officers, are able to obtain crop-growth information at crop-field level online. This unique utilization of GEE in combination with advanced analytic and extraction techniques provides a vital remote sensing tool for decision makers and scientists with a high-degree of flexibility to adapt to different uses.

Operational considerations for the application of remotely sensed forest data from LANDSAT or other airborne platforms

NASA Technical Reports Server (NTRS)

Baker, G. R.; Fethe, T. P.

1975-01-01

Research in the application of remotely sensed data from LANDSAT or other airborne platforms to the efficient management of a large timber based forest industry was divided into three phases: (1) establishment of a photo/ground sample correlation, (2) investigation of techniques for multi-spectral digital analysis, and (3) development of a semi-automated multi-level sampling system. To properly verify results, three distinct test areas were selected: (1) Jacksonville Mill Region, Lower Coastal Plain, Flatwoods, (2) Pensacola Mill Region, Middle Coastal Plain, and (3) Mississippi Mill Region, Middle Coastal Plain. The following conclusions were reached: (1) the probability of establishing an information base suitable for management requirements through a photo/ground double sampling procedure, alleviating the ground sampling effort, is encouraging, (2) known classification techniques must be investigated to ascertain the level of precision possible in separating the many densities involved, and (3) the multi-level approach must be related to an information system that is executable and feasible.

Information recovery through image sequence fusion under wavelet transformation

NASA Astrophysics Data System (ADS)

He, Qiang

2010-04-01

Remote sensing is widely applied to provide information of areas with limited ground access with applications such as to assess the destruction from natural disasters and to plan relief and recovery operations. However, the data collection of aerial digital images is constrained by bad weather, atmospheric conditions, and unstable camera or camcorder. Therefore, how to recover the information from the low-quality remote sensing images and how to enhance the image quality becomes very important for many visual understanding tasks, such like feature detection, object segmentation, and object recognition. The quality of remote sensing imagery can be improved through meaningful combination of the employed images captured from different sensors or from different conditions through information fusion. Here we particularly address information fusion to remote sensing images under multi-resolution analysis in the employed image sequences. The image fusion is to recover complete information by integrating multiple images captured from the same scene. Through image fusion, a new image with high-resolution or more perceptive for human and machine is created from a time series of low-quality images based on image registration between different video frames.

Multi-star processing and gyro filtering for the video inertial pointing system

NASA Technical Reports Server (NTRS)

Murphy, J. P.

1976-01-01

The video inertial pointing (VIP) system is being developed to satisfy the acquisition and pointing requirements of astronomical telescopes. The VIP system uses a single video sensor to provide star position information that can be used to generate three-axis pointing error signals (multi-star processing) and for input to a cathode ray tube (CRT) display of the star field. The pointing error signals are used to update the telescope's gyro stabilization system (gyro filtering). The CRT display facilitates target acquisition and positioning of the telescope by a remote operator. Linearized small angle equations are used for the multistar processing and a consideration of error performance and singularities lead to star pair location restrictions and equation selection criteria. A discrete steady-state Kalman filter which uses the integration of the gyros is developed and analyzed. The filter includes unit time delays representing asynchronous operations of the VIP microprocessor and video sensor. A digital simulation of a typical gyro stabilized gimbal is developed and used to validate the approach to the gyro filtering.

A client–server framework for 3D remote visualization of radiotherapy treatment space

PubMed Central

Santhanam, Anand P.; Min, Yugang; Dou, Tai H.; Kupelian, Patrick; Low, Daniel A.

2013-01-01

Radiotherapy is safely employed for treating wide variety of cancers. The radiotherapy workflow includes a precise positioning of the patient in the intended treatment position. While trained radiation therapists conduct patient positioning, consultation is occasionally required from other experts, including the radiation oncologist, dosimetrist, or medical physicist. In many circumstances, including rural clinics and developing countries, this expertise is not immediately available, so the patient positioning concerns of the treating therapists may not get addressed. In this paper, we present a framework to enable remotely located experts to virtually collaborate and be present inside the 3D treatment room when necessary. A multi-3D camera framework was used for acquiring the 3D treatment space. A client–server framework enabled the acquired 3D treatment room to be visualized in real-time. The computational tasks that would normally occur on the client side were offloaded to the server side to enable hardware flexibility on the client side. On the server side, a client specific real-time stereo rendering of the 3D treatment room was employed using a scalable multi graphics processing units (GPU) system. The rendered 3D images were then encoded using a GPU-based H.264 encoding for streaming. Results showed that for a stereo image size of 1280 × 960 pixels, experts with high-speed gigabit Ethernet connectivity were able to visualize the treatment space at approximately 81 frames per second. For experts remotely located and using a 100 Mbps network, the treatment space visualization occurred at 8–40 frames per second depending upon the network bandwidth. This work demonstrated the feasibility of remote real-time stereoscopic patient setup visualization, enabling expansion of high quality radiation therapy into challenging environments. PMID:23440605

Analysis of multispectral signatures and investigation of multi-aspect remote sensing techniques

NASA Technical Reports Server (NTRS)

Malila, W. A.; Hieber, R. H.; Sarno, J. E.

1974-01-01

Two major aspects of remote sensing with multispectral scanners (MSS) are investigated. The first, multispectral signature analysis, includes the effects on classification performance of systematic variations found in the average signals received from various ground covers as well as the prediction of these variations with theoretical models of physical processes. The foremost effects studied are those associated with the time of day airborne MSS data are collected. Six data collection runs made over the same flight line in a period of five hours are analyzed, it is found that the time span significantly affects classification performance. Variations associated with scan angle also are studied. The second major topic of discussion is multi-aspect remote sensing, a new concept in remote sensing with scanners. Here, data are collected on multiple passes by a scanner that can be tilted to scan forward of the aircraft at different angles on different passes. The use of such spatially registered data to achieve improved classification of agricultural scenes is investigated and found promising. Also considered are the possibilities of extracting from multi-aspect data, information on the condition of corn canopies and the stand characteristics of forests.

A Comparative Distributed Evaluation of the NWS-RDHM using Shape Matching and Traditional Measures with In Situ and Remotely Sensed Information

NASA Astrophysics Data System (ADS)

KIM, J.; Bastidas, L. A.

2011-12-01

We evaluate, calibrate and diagnose the performance of National Weather Service RDHM distributed model over the Durango River Basin in Colorado using simultaneously in situ and remotely sensed information from different discharge gaging stations (USGS), information about snow cover (SCV) and snow water equivalent (SWE) in situ from several SNOTEL sites and snow information distributed over the catchment from remotely sensed information (NOAA-NASA). In the process of evaluation we attempt to establish the optimal degree of parameter distribution over the catchment by calibration. A multi-criteria approach based on traditional measures (RMSE) and similarity based pattern comparisons using the Hausdorff and Earth Movers Distance approaches is used for the overall evaluation of the model performance. These pattern based approaches (shape matching) are found to be extremely relevant to account for the relatively large degree of inaccuracy in the remotely sensed SWE (judged inaccurate in terms of the value but reliable in terms of the distribution pattern) and the high reliability of the SCV (yes/no situation) while at the same time allow for an evaluation that quantifies the accuracy of the model over the entire catchment considering the different types of observations. The Hausdorff norm, due to its intrinsically multi-dimensional nature, allows for the incorporation of variables such as the terrain elevation as one of the variables for evaluation. The EMD, because of its extremely high computational overburden, requires the mapping of the set of evaluation variables into a two dimensional matrix for computation.

Troubleshooting RSIG

EPA Pesticide Factsheets

Remote Sensing Information Gateway, a tool that allows scientists, researchers and decision makers to access a variety of multi-terabyte, environmental datasets and to subset the data and obtain only needed variables, greatly improving the download time.

A Prototype Hydrologic Observatory for the Neuse River Basin Using Remote Sensing Data as a Part of the CUAHSI-HIS Effort

NASA Astrophysics Data System (ADS)

Kanwar, R.; Narayan, U.; Lakshmi, V.

2005-12-01

Remote sensing has the potential to immensely advance the science and application of hydrology as it provides multi-scale and multi-temporal measurements of several hydrologic parameters. There is a wide variety of remote sensing data sources available to a hydrologist with a myriad of data formats, access techniques, data quality issues and temporal and spatial extents. It is very important to make data availability and its usage as convenient as possible for potential users. The CUAHSI Hydrologic Information System (HIS) initiative addresses this issue of better data access and management for hydrologists with a focus on in-situ data, that is point measurements of water and energy fluxes which make up the 'more conventional' sources of hydrologic data. This paper explores various sources of remotely sensed hydrologic data available, their data formats and volumes, current modes of data acquisition by end users, metadata associated with data itself, and requirements from potential data models that would allow a seamless integration of remotely sensed hydrologic observations into the Hydrologic Information System. Further, a prototype hydrologic observatory (HO) for the Neuse River Basin is developed using surface temperature, vegetation indices and soil moisture estimates available from remote sensing. The prototype (HO) uses the CUAHSI digital library system (DLS) on the back (server) end. On the front (client) end, a rich visual environment has been developed in order to provide better decision making tools in order to make an optimal choice in the selection of remote sensing data for a particular application. An easy point and click interface to the remote sensing data is also implemented for common users who are just interested in location based query of hydrologic variable values.

RSIG Data Inventory

EPA Pesticide Factsheets

Remote Sensing Information Gateway, a tool that allows scientists, researchers and decision makers to access a variety of multi-terabyte, environmental datasets and to subset the data and obtain only needed variables, greatly improving the download time.

RSIG Video Demonstrations

EPA Pesticide Factsheets

Remote Sensing Information Gateway, a tool that allows scientists, researchers and decision makers to access a variety of multi-terabyte, environmental datasets and to subset the data and obtain only needed variables, greatly improving the download time.

How RSIG Regrids Data

EPA Pesticide Factsheets

Remote Sensing Information Gateway, a tool that allows scientists, researchers and decision makers to access a variety of multi-terabyte, environmental datasets and to subset the data and obtain only needed variables, greatly improving the download time.

The birth of quantum networks: merging remote entanglement with local multi-qubit control

NASA Astrophysics Data System (ADS)

Hanson, Ronald

The realization of a highly connected network of qubit registers is a central challenge for quantum information processing and long-distance quantum communication. Diamond spins associated with NV centers are promising building blocks for such a network: they combine a coherent spin-photon interface that has already enabled creation of spin-spin entanglement over 1km with a local register of robust and well-controlled nuclear spin qubits for information processing and error correction. We are now entering a new research stage in which we can exploit these features simultaneously and build multi-qubit networks. I will present our latest results towards the first of such experiments: entanglement distillation between remote quantum network nodes. Finally, I will discuss the challenges and opportunities ahead on the road to large-scale networks of qubit registers for quantum computation and communication.

Surveillance of Arthropod Vector-Borne Infectious Diseases Using Remote Sensing Techniques: A Review

PubMed Central

Kalluri, Satya; Gilruth, Peter; Rogers, David; Szczur, Martha

2007-01-01

Epidemiologists are adopting new remote sensing techniques to study a variety of vector-borne diseases. Associations between satellite-derived environmental variables such as temperature, humidity, and land cover type and vector density are used to identify and characterize vector habitats. The convergence of factors such as the availability of multi-temporal satellite data and georeferenced epidemiological data, collaboration between remote sensing scientists and biologists, and the availability of sophisticated, statistical geographic information system and image processing algorithms in a desktop environment creates a fertile research environment. The use of remote sensing techniques to map vector-borne diseases has evolved significantly over the past 25 years. In this paper, we review the status of remote sensing studies of arthropod vector-borne diseases due to mosquitoes, ticks, blackflies, tsetse flies, and sandflies, which are responsible for the majority of vector-borne diseases in the world. Examples of simple image classification techniques that associate land use and land cover types with vector habitats, as well as complex statistical models that link satellite-derived multi-temporal meteorological observations with vector biology and abundance, are discussed here. Future improvements in remote sensing applications in epidemiology are also discussed. PMID:17967056

Detecting representations of recent and remote autobiographical memories in vmPFC and hippocampus

PubMed Central

Bonnici, Heidi M.; Chadwick, Martin J.; Lutti, Antoine; Hassabis, Demis; Weiskopf, Nikolaus; Maguire, Eleanor A.

2012-01-01

How autobiographical memories are represented in the human brain and whether this changes with time are questions central to memory neuroscience. Two regions in particular have been consistently implicated, the ventromedial prefrontal cortex (vmPFC) and the hippocampus, although their precise contributions are still contested. The key question in this debate, when reduced to its simplest form, concerns where information about specific autobiographical memories is located. Here we availed ourselves of the opportunity afforded by multi-voxel pattern analysis (MVPA) to provide an alternative to conventional neuropsychological and fMRI approaches, by detecting representations of individual autobiographical memories in patterns of fMRI activity. We examined whether information about specific recent (two weeks old) and remote (ten years old) autobiographical memories was represented in vmPFC and hippocampus, and other medial temporal and neocortical regions. vmPFC contained information about recent and remote autobiographical memories, although remote memories were more readily detected there, indicating that consolidation or a change of some kind had occurred. Information about both types of memory was also present in the hippocampus, suggesting it plays a role in the retrieval of vivid autobiographical memories regardless of remoteness. Interestingly, we also found that while recent and remote memories were both represented within anterior and posterior hippocampus, the latter nevertheless contained more information about remote memories. Thus, like vmPFC, the hippocampus too respected the distinction between recent and remote memories. Overall, these findings clarify and extend our view of vmPFC and hippocampus while also informing systems-level consolidation and providing clear targets for future studies. PMID:23175849

Lyceum: A Multi-Protocol Digital Library Gateway

NASA Technical Reports Server (NTRS)

Maa, Ming-Hokng; Nelson, Michael L.; Esler, Sandra L.

1997-01-01

Lyceum is a prototype scalable query gateway that provides a logically central interface to multi-protocol and physically distributed, digital libraries of scientific and technical information. Lyceum processes queries to multiple syntactically distinct search engines used by various distributed information servers from a single logically central interface without modification of the remote search engines. A working prototype (http://www.larc.nasa.gov/lyceum/) demonstrates the capabilities, potentials, and advantages of this type of meta-search engine by providing access to over 50 servers covering over 20 disciplines.

Identification of pests and diseases of Dalbergia hainanensis based on EVI time series and classification of decision tree

NASA Astrophysics Data System (ADS)

Luo, Qiu; Xin, Wu; Qiming, Xiong

2017-06-01

In the process of vegetation remote sensing information extraction, the problem of phenological features and low performance of remote sensing analysis algorithm is not considered. To solve this problem, the method of remote sensing vegetation information based on EVI time-series and the classification of decision-tree of multi-source branch similarity is promoted. Firstly, to improve the time-series stability of recognition accuracy, the seasonal feature of vegetation is extracted based on the fitting span range of time-series. Secondly, the decision-tree similarity is distinguished by adaptive selection path or probability parameter of component prediction. As an index, it is to evaluate the degree of task association, decide whether to perform migration of multi-source decision tree, and ensure the speed of migration. Finally, the accuracy of classification and recognition of pests and diseases can reach 87%--98% of commercial forest in Dalbergia hainanensis, which is significantly better than that of MODIS coverage accuracy of 80%--96% in this area. Therefore, the validity of the proposed method can be verified.

Web Access to RSIG Data

EPA Pesticide Factsheets

Remote Sensing Information Gateway, a tool that allows scientists, researchers and decision makers to access a variety of multi-terabyte, environmental datasets and to subset the data and obtain only needed variables, greatly improving the download time.

Tips for Running RSIG2D

EPA Pesticide Factsheets

Remote Sensing Information Gateway, a tool that allows scientists, researchers and decision makers to access a variety of multi-terabyte, environmental datasets and to subset the data and obtain only needed variables, greatly improving the download time.

Measuring deforestation using remote sensing and its implication for conservation in Gunung Palung National Park, West Kalimantan, Indonesia

NASA Astrophysics Data System (ADS)

Fawzi, N. I.; Husna, V. N.; Helms, J. A.

2018-05-01

Gunung Palung National Park (1,080 km2, 1°3’ – 1°22’ S, 109°54’ – 110°28’ E) was first protected in 1937 and is now one of the largest remaining primary lowland mixed dipterocarp forests on Borneo. To help inform conservation efforts, we measured forest cover change in the protected area using 11 multi-temporal Landsat series images with path/row 121/61. Annual deforestation rates have declined since measurement began in 1989, to around 68 hectares per year in 2011 and 112 hectares per year in 2017. Halting deforestation in this protected area requires to tackle its underlying economic and social causes, and find ways for communities to meet their needs without resorting to forest clearing. Community empowerment, forest rehabilitation, and health care incentives as payment for ecosystem services can help reduce deforestation in Gunung Palung National Park. This becomes a positive trend which we must continue to always work in forest conservation. Future forest monitoring will be dependency with remote sensing analysis and open source remote sensing data such as Landsat and Sentinel data remain an important data source for historical forest change monitoring.

Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing

DTIC Science & Technology

2016-07-15

AFRL-AFOSR-JP-TR-2016-0068 Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing Hean-Teik...SUBTITLE Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER... electromagnetics to the application in microwave remote sensing as well as extension of modelling capability with computational flexibility to study

Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing

DTIC Science & Technology

2016-07-15

AFRL-AFOSR-JP-TR-2016-0068 Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing Hean-Teik...SUBTITLE Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER...electromagnetics to the application in microwave remote sensing as well as extension of modelling capability with computational flexibility to study

Multi-service terminal adapter based on IP technology applications in rural area

NASA Astrophysics Data System (ADS)

Gao, Li; Li, Xiaobo; Yan, Juntao; Ren, Xupeng

Take advantage of ample modern existing telecom network resources to rural areas may achieve it's information society gradually. This includes the establishment of integrated rural information service platform, modern remote education center and electronic administration management platform for rural areas. The geographical and economic constraints must be overcome for structuring the rural service support system, in order to provide technical support, information products and information services to modern rural information service system. It is important that development an access platform based IP technology, which supports multi-service access in order to implement a variety of types of mobile terminal equipment adapter access and to reduce restrictions on mobile terminal equipment.

Development of a fusion approach selection tool

NASA Astrophysics Data System (ADS)

Pohl, C.; Zeng, Y.

2015-06-01

During the last decades number and quality of available remote sensing satellite sensors for Earth observation has grown significantly. The amount of available multi-sensor images along with their increased spatial and spectral resolution provides new challenges to Earth scientists. With a Fusion Approach Selection Tool (FAST) the remote sensing community would obtain access to an optimized and improved image processing technology. Remote sensing image fusion is a mean to produce images containing information that is not inherent in the single image alone. In the meantime the user has access to sophisticated commercialized image fusion techniques plus the option to tune the parameters of each individual technique to match the anticipated application. This leaves the operator with an uncountable number of options to combine remote sensing images, not talking about the selection of the appropriate images, resolution and bands. Image fusion can be a machine and time-consuming endeavour. In addition it requires knowledge about remote sensing, image fusion, digital image processing and the application. FAST shall provide the user with a quick overview of processing flows to choose from to reach the target. FAST will ask for available images, application parameters and desired information to process this input to come out with a workflow to quickly obtain the best results. It will optimize data and image fusion techniques. It provides an overview on the possible results from which the user can choose the best. FAST will enable even inexperienced users to use advanced processing methods to maximize the benefit of multi-sensor image exploitation.

The Impact of Estimating High-Resolution Tropospheric Gradients on Multi-GNSS Precise Positioning

PubMed Central

Zhou, Feng; Li, Xingxing; Li, Weiwei; Chen, Wen; Dong, Danan; Wickert, Jens; Schuh, Harald

2017-01-01

Benefits from the modernized US Global Positioning System (GPS), the revitalized Russian GLObal NAvigation Satellite System (GLONASS), and the newly-developed Chinese BeiDou Navigation Satellite System (BDS) and European Galileo, multi-constellation Global Navigation Satellite System (GNSS) has emerged as a powerful tool not only in positioning, navigation, and timing (PNT), but also in remote sensing of the atmosphere and ionosphere. Both precise positioning and the derivation of atmospheric parameters can benefit from multi-GNSS observations. In this contribution, extensive evaluations are conducted with multi-GNSS datasets collected from 134 globally-distributed ground stations of the International GNSS Service (IGS) Multi-GNSS Experiment (MGEX) network in July 2016. The datasets are processed in six different constellation combinations, i.e., GPS-, GLONASS-, BDS-only, GPS + GLONASS, GPS + BDS, and GPS + GLONASS + BDS + Galileo precise point positioning (PPP). Tropospheric gradients are estimated with eight different temporal resolutions, from 1 h to 24 h, to investigate the impact of estimating high-resolution gradients on position estimates. The standard deviation (STD) is used as an indicator of positioning repeatability. The results show that estimating tropospheric gradients with high temporal resolution can achieve better positioning performance than the traditional strategy in which tropospheric gradients are estimated on a daily basis. Moreover, the impact of estimating tropospheric gradients with different temporal resolutions at various elevation cutoff angles (from 3° to 20°) is investigated. It can be observed that with increasing elevation cutoff angles, the improvement in positioning repeatability is decreased. PMID:28368346

The Impact of Estimating High-Resolution Tropospheric Gradients on Multi-GNSS Precise Positioning.

PubMed

Zhou, Feng; Li, Xingxing; Li, Weiwei; Chen, Wen; Dong, Danan; Wickert, Jens; Schuh, Harald

2017-04-03

Benefits from the modernized US Global Positioning System (GPS), the revitalized Russian GLObal NAvigation Satellite System (GLONASS), and the newly-developed Chinese BeiDou Navigation Satellite System (BDS) and European Galileo, multi-constellation Global Navigation Satellite System (GNSS) has emerged as a powerful tool not only in positioning, navigation, and timing (PNT), but also in remote sensing of the atmosphere and ionosphere. Both precise positioning and the derivation of atmospheric parameters can benefit from multi-GNSS observations. In this contribution, extensive evaluations are conducted with multi-GNSS datasets collected from 134 globally-distributed ground stations of the International GNSS Service (IGS) Multi-GNSS Experiment (MGEX) network in July 2016. The datasets are processed in six different constellation combinations, i.e., GPS-, GLONASS-, BDS-only, GPS + GLONASS, GPS + BDS, and GPS + GLONASS + BDS + Galileo precise point positioning (PPP). Tropospheric gradients are estimated with eight different temporal resolutions, from 1 h to 24 h, to investigate the impact of estimating high-resolution gradients on position estimates. The standard deviation (STD) is used as an indicator of positioning repeatability. The results show that estimating tropospheric gradients with high temporal resolution can achieve better positioning performance than the traditional strategy in which tropospheric gradients are estimated on a daily basis. Moreover, the impact of estimating tropospheric gradients with different temporal resolutions at various elevation cutoff angles (from 3° to 20°) is investigated. It can be observed that with increasing elevation cutoff angles, the improvement in positioning repeatability is decreased.

[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.

High Throughput Biological Analysis Using Multi-bit Magnetic Digital Planar Tags

NASA Astrophysics Data System (ADS)

Hong, B.; Jeong, J.-R.; Llandro, J.; Hayward, T. J.; Ionescu, A.; Trypiniotis, T.; Mitrelias, T.; Kopper, K. P.; Steinmuller, S. J.; Bland, J. A. C.

2008-06-01

We report a new magnetic labelling technology for high-throughput biomolecular identification and DNA sequencing. Planar multi-bit magnetic tags have been designed and fabricated, which comprise a magnetic barcode formed by an ensemble of micron-sized thin film Ni80Fe20 bars encapsulated in SU8. We show that by using a globally applied magnetic field and magneto-optical Kerr microscopy the magnetic elements in the multi-bit magnetic tags can be addressed individually and encoded/decoded remotely. The critical steps needed to show the feasibility of this technology are demonstrated, including fabrication, flow transport, remote writing and reading, and successful functionalization of the tags as verified by fluorescence detection. This approach is ideal for encoding information on tags in microfluidic flow or suspension, for such applications as labelling of chemical precursors during drug synthesis and combinatorial library-based high-throughput multiplexed bioassays.

Advances in multi-sensor data fusion: algorithms and applications.

PubMed

Dong, Jiang; Zhuang, Dafang; Huang, Yaohuan; Fu, Jingying

2009-01-01

With the development of satellite and remote sensing techniques, more and more image data from airborne/satellite sensors have become available. Multi-sensor image fusion seeks to combine information from different images to obtain more inferences than can be derived from a single sensor. In image-based application fields, image fusion has emerged as a promising research area since the end of the last century. The paper presents an overview of recent advances in multi-sensor satellite image fusion. Firstly, the most popular existing fusion algorithms are introduced, with emphasis on their recent improvements. Advances in main applications fields in remote sensing, including object identification, classification, change detection and maneuvering targets tracking, are described. Both advantages and limitations of those applications are then discussed. Recommendations are addressed, including: (1) Improvements of fusion algorithms; (2) Development of "algorithm fusion" methods; (3) Establishment of an automatic quality assessment scheme.

Change Detection of High-Resolution Remote Sensing Images Based on Adaptive Fusion of Multiple Features

NASA Astrophysics Data System (ADS)

Wang, G. H.; Wang, H. B.; Fan, W. F.; Liu, Y.; Chen, C.

2018-04-01

In view of the traditional change detection algorithm mainly depends on the spectral information image spot, failed to effectively mining and fusion of multi-image feature detection advantage, the article borrows the ideas of object oriented analysis proposed a multi feature fusion of remote sensing image change detection algorithm. First by the multi-scale segmentation of image objects based; then calculate the various objects of color histogram and linear gradient histogram; utilizes the color distance and edge line feature distance between EMD statistical operator in different periods of the object, using the adaptive weighted method, the color feature distance and edge in a straight line distance of combination is constructed object heterogeneity. Finally, the curvature histogram analysis image spot change detection results. The experimental results show that the method can fully fuse the color and edge line features, thus improving the accuracy of the change detection.

Integrating remote sensing, geographic information systems and global positioning system techniques with hydrological modeling

NASA Astrophysics Data System (ADS)

Thakur, Jay Krishna; Singh, Sudhir Kumar; Ekanthalu, Vicky Shettigondahalli

2017-07-01

Integration of remote sensing (RS), geographic information systems (GIS) and global positioning system (GPS) are emerging research areas in the field of groundwater hydrology, resource management, environmental monitoring and during emergency response. Recent advancements in the fields of RS, GIS, GPS and higher level of computation will help in providing and handling a range of data simultaneously in a time- and cost-efficient manner. This review paper deals with hydrological modeling, uses of remote sensing and GIS in hydrological modeling, models of integrations and their need and in last the conclusion. After dealing with these issues conceptually and technically, we can develop better methods and novel approaches to handle large data sets and in a better way to communicate information related with rapidly decreasing societal resources, i.e. groundwater.

An algorithm for retrieving rock-desertification from multispectral remote sensing images

NASA Astrophysics Data System (ADS)

Xia, Xueqi; Tian, Qingjiu; Liao, Yan

2009-06-01

Rock-desertification is a typical environmental and ecological problem in Southwest China. As remote sensing is an important means of monitoring spatial variation of rock-desertification, a method is developed for measurement and information retrieval of rock-desertification from multi-spectral high-resolution remote sensing images. MNF transform is applied to 4-band IKONOS multi-spectral remotely sensed data to reduce the number of spectral dimensions to three. In the 3-demension endmembers are extracted and analyzed. It is found that various vegetations group into a line defined as "vegetation line", in which "dark vegetations", such as coniferous forest and broadleaf forest, continuously change to "bright vegetations", such as grasses. It is presumed that is caused by deferent proportion of shadow mixed in leaves or branches in various types of vegetation. Normalized distance between the endmember of rocks and the vegetation line is defined as Geometric Rock-desertification Index (GRI), which was used to scale rock-desertification. The case study with ground truth validation in Puding, Guizhou province showed successes and the advantages of this method.

Implementation of Sentinel-2 Data in the M4Land System for the Generation of Continuous Information Products in Agriculture

NASA Astrophysics Data System (ADS)

Klug, P.; Schlenz, F.; Hank, T.; Migdall, S.; Weiß, I.; Danner, M.; Bach, H.; Mauser, W.

2016-08-01

The analysis system developed in the frame of the M4Land project (Model based, Multi-temporal, Multi scale and Multi sensorial retrieval of continuous land management information) has proven its capabilities of classifying crop type and creating products on the intensity of agricultural production using optical remote sensing data from Landsat and RapidEye. In this study, Sentinel-2 data is used for the first time together with Landsat 7 ETM+ and 8 OLI data within the M4Land analysis system to derive continuously crop type and the agricultural intensity of fields in an area north of Munich, Germany and the year 2015.

Operating tool for a distributed data and information management system

NASA Astrophysics Data System (ADS)

Reck, C.; Mikusch, E.; Kiemle, S.; Wolfmüller, M.; Böttcher, M.

2002-07-01

The German Remote Sensing Data Center has developed the Data Information and Management System DIMS which provides multi-mission ground system services for earth observation product processing, archiving, ordering and delivery. DIMS successfully uses newest technologies within its services. This paper presents the solution taken to simplify operation tasks for this large and distributed system.

Simultaneous multi-beam planar array IR (pair) spectroscopy

DOEpatents

Elmore, Douglas L.; Rabolt, John F.; Tsao, Mei-Wei

2005-09-13

An apparatus and method capable of providing spatially multiplexed IR spectral information simultaneously in real-time for multiple samples or multiple spatial areas of one sample using IR absorption phenomena requires no moving parts or Fourier Transform during operation, and self-compensates for background spectra and degradation of component performance over time. IR spectral information and chemical analysis of the samples is determined by using one or more IR sources, sampling accessories for positioning the samples, optically dispersive elements, a focal plane array (FPA) arranged to detect the dispersed light beams, and a processor and display to control the FPA, and display an IR spectrograph. Fiber-optic coupling can be used to allow remote sensing. Portability, reliability, and ruggedness is enhanced due to the no-moving part construction. Applications include determining time-resolved orientation and characteristics of materials, including polymer monolayers. Orthogonal polarizers may be used to determine certain material characteristics.

Neural mechanism underlying autobiographical memory modulated by remoteness and emotion

NASA Astrophysics Data System (ADS)

Ge, Ruiyang; Fu, Yan; Wang, DaHua; Yao, Li; Long, Zhiying

2012-03-01

Autobiographical memory is the ability to recollect past events from one's own life. Both emotional tone and memory remoteness can influence autobiographical memory retrieval along the time axis of one's life. Although numerous studies have been performed to investigate brain regions involved in retrieving processes of autobiographical memory, the effect of emotional tone and memory age on autobiographical memory retrieval remains to be clarified. Moreover, whether the involvement of hippocampus in consolidation of autobiographical events is time dependent or independent has been controversial. In this study, we investigated the effect of memory remoteness (factor1: recent and remote) and emotional valence (factor2: positive and negative) on neural correlates underlying autobiographical memory by using functional magnetic resonance imaging (fMRI) technique. Although all four conditions activated some common regions known as "core" regions in autobiographical memory retrieval, there are some other regions showing significantly different activation for recent versus remote and positive versus negative memories. In particular, we found that bilateral hippocampal regions were activated in the four conditions regardless of memory remoteness and emotional valence. Thus, our study confirmed some findings of previous studies and provided further evidence to support the multi-trace theory which believes that the role of hippocampus involved in autobiographical memory retrieval is time-independent and permanent in memory consolidation.

Multispectral, hyperspectral, and LiDAR remote sensing and geographic information fusion for improved earthquake response

NASA Astrophysics Data System (ADS)

Kruse, F. A.; Kim, A. M.; Runyon, S. C.; Carlisle, Sarah C.; Clasen, C. C.; Esterline, C. H.; Jalobeanu, A.; Metcalf, J. P.; Basgall, P. L.; Trask, D. M.; Olsen, R. C.

2014-06-01

The Naval Postgraduate School (NPS) Remote Sensing Center (RSC) and research partners have completed a remote sensing pilot project in support of California post-earthquake-event emergency response. The project goals were to dovetail emergency management requirements with remote sensing capabilities to develop prototype map products for improved earthquake response. NPS coordinated with emergency management services and first responders to compile information about essential elements of information (EEI) requirements. A wide variety of remote sensing datasets including multispectral imagery (MSI), hyperspectral imagery (HSI), and LiDAR were assembled by NPS for the purpose of building imagery baseline data; and to demonstrate the use of remote sensing to derive ground surface information for use in planning, conducting, and monitoring post-earthquake emergency response. Worldview-2 data were converted to reflectance, orthorectified, and mosaicked for most of Monterey County; CA. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data acquired at two spatial resolutions were atmospherically corrected and analyzed in conjunction with the MSI data. LiDAR data at point densities from 1.4 pts/m2 to over 40 points/ m2 were analyzed to determine digital surface models. The multimodal data were then used to develop change detection approaches and products and other supporting information. Analysis results from these data along with other geographic information were used to identify and generate multi-tiered products tied to the level of post-event communications infrastructure (internet access + cell, cell only, no internet/cell). Technology transfer of these capabilities to local and state emergency response organizations gives emergency responders new tools in support of post-disaster operational scenarios.

Feasibility of a novel participatory multi-sector continuous improvement approach to enhance food security in remote Indigenous Australian communities.

PubMed

Brimblecombe, J; Bailie, R; van den Boogaard, C; Wood, B; Liberato, S C; Ferguson, M; Coveney, J; Jaenke, R; Ritchie, J

2017-12-01

Food insecurity underlies and compounds many of the development issues faced by remote Indigenous communities in Australia. Multi-sector approaches offer promise to improve food security. We assessed the feasibility of a novel multi-sector approach to enhance community food security in remote Indigenous Australia. A longitudinal comparative multi-site case study, the Good Food Systems Good Food for All Project, was conducted (2009-2013) with four Aboriginal communities. Continuous improvement meetings were held in each community. Data from project documents and store sales were used to assess feasibility according to engagement, uptake and sustainability of action, and impact on community diet, as well as identifying conditions facilitating or hindering these. Engagement was established where: the community perceived a need for the approach; where trust was developed between the community and facilitators; where there was community stability; and where flexibility was applied in the timing of meetings. The approach enabled stakeholders in each community to collectively appraise the community food system and plan action. Actions that could be directly implemented within available resources resulted from developing collaborative capacity. Actions requiring advocacy, multi-sectoral involvement, commitment or further resources were less frequently used. Positive shifts in community diet were associated with key areas where actions were implemented. A multi-sector participatory approach seeking continuous improvement engaged committed Aboriginal and non-Aboriginal stakeholders and was shown to have potential to shift community diet. Provision of clear mechanisms to link this approach with higher level policy and decision-making structures, clarity of roles and responsibilities, and processes to prioritise and communicate actions across sectors should further strengthen capacity for food security improvement. Integrating this approach enabling local decision-making into community governance structures with adequate resourcing is an imperative.

Beacon data acquisition and display system

DOEpatents

Skogmo, D.G.; Black, B.D.

1991-12-17

A system for transmitting aircraft beacon information received by a secondary surveillance radar through telephone lines to a remote display includes a digitizer connected to the radar for preparing a serial file of data records containing position and identification information of the beacons detected by each sweep of the radar. This information is transmitted through the telephone lines to a remote computer where it is displayed. 6 figures.

Beacon data acquisition and display system

DOEpatents

Skogmo, David G.; Black, Billy D.

1991-01-01

A system for transmitting aircraft beacon information received by a secondary surveillance radar through telephone lines to a remote display includes a digitizer connected to the radar for preparing a serial file of data records containing position and identification information of the beacons detected by each sweep of the radar. This information is transmitted through the telephone lines to a remote computer where it is displayed.

Goddard Atmospheric Composition Data Center: Aura Data and Services in One Place

NASA Technical Reports Server (NTRS)

Leptoukh, G.; Kempler, S.; Gerasimov, I.; Ahmad, S.; Johnson, J.

2005-01-01

The Goddard Atmospheric Composition Data and Information Services Center (AC-DISC) is a portal to the Atmospheric Composition specific, user driven, multi-sensor, on-line, easy access archive and distribution system employing data analysis and visualization, data mining, and other user requested techniques for the better science data usage. It provides convenient access to Atmospheric Composition data and information from various remote-sensing missions, from TOMS, UARS, MODIS, and AIRS, to the most recent data from Aura OMI, MLS, HIRDLS (once these datasets are released to the public), as well as Atmospheric Composition datasets residing at other remote archive site.

Large scale maps of cropping intensity in Asia from MODIS

NASA Astrophysics Data System (ADS)

Gray, J. M.; Friedl, M. A.; Frolking, S. E.; Ramankutty, N.; Nelson, A.

2013-12-01

Agricultural systems are geographically extensive, have profound significance to society, and also affect regional energy, carbon, and water cycles. Since most suitable lands worldwide have been cultivated, there is growing pressure to increase yields on existing agricultural lands. In tropical and sub-tropical regions, multi-cropping is widely used to increase food production, but regional-to-global information related to multi-cropping practices is poor. Such information is of critical importance to ensure sustainable food production while mitigating against negative environmental impacts associated with agriculture such as contamination and depletion of freshwater resources. Unfortunately, currently available large-area inventory statistics are inadequate because they do not capture important spatial patterns in multi-cropping, and are generally not available in a timeframe that can be used to help manage cropping systems. High temporal resolution sensors such as MODIS provide an excellent source of information for addressing this need. However, relative to studies that document agricultural extensification, systematic assessment of agricultural intensification via multi-cropping has received relatively little attention. The goal of this work is to help close this methodological and information gap by developing methods that use multi-temporal remote sensing to map multi-cropping systems in Asia. Image time series analysis is especially challenging in Asia because atmospheric conditions including clouds and aerosols lead to high frequencies of missing or low quality remote sensing observations, especially during the Asian Monsoon. The methodology that we use for this work builds upon the algorithm used to produce the MODIS Land Cover Dynamics product (MCD12Q2), but employs refined methods to segment, smooth, and gap-fill 8-day EVI time series calculated from MODIS BRDF corrected surface reflectances. Crop cycle segments are identified based on changes in slope for linear regressions estimated for local windows, and constrained by the EVI amplitude and length of crop cycles that are identified. The procedure can be used to map seasonal or long-term average cropping strategies, and to characterize changes in cropping intensity over longer time periods. The datasets produced using this method therefore provide information related to global cropping systems, and more broadly, provide important information that is required to ensure sustainable management of Earth's resources and ensure food security. To test our algorithm, we applied it to time series of MODIS EVI images over Asia from 2000-2012. Our results demonstrate the utility of multi-temporal remote sensing for characterizing multi-cropping practices in some of the most important and intensely agricultural regions in the world. To evaluate our approach, we compared results from MODIS to field-scale survey data at the pixel scale, and agricultural inventory statistics at sub-national scales. We then mapped changes in multi-cropped area in Asia from the early MODIS period (2001-2004) to present (2009-2012), and characterizes the magnitude and location of changes in cropping intensity over the last 12 years. We conclude with a discussion of the challenges, future improvements, and broader impacts of this work.

THE IDEA IS TO USEMODIS IN CONJUNCTION WITH THE CURRENT LIMITED LANDSAT CAPABILITY, COMMERCIAL SATELLITES, ANDUNMANNED AERIAL VEHICLES (UAV), IN A MULTI-STAGE APPROACH TO MEET EPA INFORMATION NEEDS.REMOTE SENSING OVERVIEW: EPA CAPABILITIES, PRIORITY AGENCY APPLICATIONS, SENSOR/AIRCRAFT CAPABILITIES, COST CONSIDERATIONS, SPECTRAL AND SPATIAL RESOLUTIONS, AND TEMPORAL CONSIDERATIONS

EPA Science Inventory

EPA remote sensing capabilities include applied research for priority applications and technology support for operational assistance to clients across the Agency. The idea is to use MODIS in conjunction with the current limited Landsat capability, commercial satellites, and Unma...

Criteria for the optimal selection of remote sensing optical images to map event landslides

NASA Astrophysics Data System (ADS)

Fiorucci, Federica; Giordan, Daniele; Santangelo, Michele; Dutto, Furio; Rossi, Mauro; Guzzetti, Fausto

2018-01-01

Landslides leave discernible signs on the land surface, most of which can be captured in remote sensing images. Trained geomorphologists analyse remote sensing images and map landslides through heuristic interpretation of photographic and morphological characteristics. Despite a wide use of remote sensing images for landslide mapping, no attempt to evaluate how the image characteristics influence landslide identification and mapping exists. This paper presents an experiment to determine the effects of optical image characteristics, such as spatial resolution, spectral content and image type (monoscopic or stereoscopic), on landslide mapping. We considered eight maps of the same landslide in central Italy: (i) six maps obtained through expert heuristic visual interpretation of remote sensing images, (ii) one map through a reconnaissance field survey, and (iii) one map obtained through a real-time kinematic (RTK) differential global positioning system (dGPS) survey, which served as a benchmark. The eight maps were compared pairwise and to a benchmark. The mismatch between each map pair was quantified by the error index, E. Results show that the map closest to the benchmark delineation of the landslide was obtained using the higher resolution image, where the landslide signature was primarily photographical (in the landslide source and transport area). Conversely, where the landslide signature was mainly morphological (in the landslide deposit) the best mapping result was obtained using the stereoscopic images. Albeit conducted on a single landslide, the experiment results are general, and provide useful information to decide on the optimal imagery for the production of event, seasonal and multi-temporal landslide inventory maps.

Automatic registration of optical imagery with 3d lidar data using local combined mutual information

NASA Astrophysics Data System (ADS)

Parmehr, E. G.; Fraser, C. S.; Zhang, C.; Leach, J.

2013-10-01

Automatic registration of multi-sensor data is a basic step in data fusion for photogrammetric and remote sensing applications. The effectiveness of intensity-based methods such as Mutual Information (MI) for automated registration of multi-sensor image has been previously reported for medical and remote sensing applications. In this paper, a new multivariable MI approach that exploits complementary information of inherently registered LiDAR DSM and intensity data to improve the robustness of registering optical imagery and LiDAR point cloud, is presented. LiDAR DSM and intensity information has been utilised in measuring the similarity of LiDAR and optical imagery via the Combined MI. An effective histogramming technique is adopted to facilitate estimation of a 3D probability density function (pdf). In addition, a local similarity measure is introduced to decrease the complexity of optimisation at higher dimensions and computation cost. Therefore, the reliability of registration is improved due to the use of redundant observations of similarity. The performance of the proposed method for registration of satellite and aerial images with LiDAR data in urban and rural areas is experimentally evaluated and the results obtained are discussed.

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.

Remotely sensed rice yield prediction using multi-temporal NDVI data derived from NOAA's-AVHRR.

PubMed

Huang, Jingfeng; Wang, Xiuzhen; Li, Xinxing; Tian, Hanqin; Pan, Zhuokun

2013-01-01

Grain-yield prediction using remotely sensed data have been intensively studied in wheat and maize, but such information is limited in rice, barley, oats and soybeans. The present study proposes a new framework for rice-yield prediction, which eliminates the influence of the technology development, fertilizer application, and management improvement and can be used for the development and implementation of provincial rice-yield predictions. The technique requires the collection of remotely sensed data over an adequate time frame and a corresponding record of the region's crop yields. Longer normalized-difference-vegetation-index (NDVI) time series are preferable to shorter ones for the purposes of rice-yield prediction because the well-contrasted seasons in a longer time series provide the opportunity to build regression models with a wide application range. A regression analysis of the yield versus the year indicated an annual gain in the rice yield of 50 to 128 kg ha(-1). Stepwise regression models for the remotely sensed rice-yield predictions have been developed for five typical rice-growing provinces in China. The prediction models for the remotely sensed rice yield indicated that the influences of the NDVIs on the rice yield were always positive. The association between the predicted and observed rice yields was highly significant without obvious outliers from 1982 to 2004. Independent validation found that the overall relative error is approximately 5.82%, and a majority of the relative errors were less than 5% in 2005 and 2006, depending on the study area. The proposed models can be used in an operational context to predict rice yields at the provincial level in China. The methodologies described in the present paper can be applied to any crop for which a sufficient time series of NDVI data and the corresponding historical yield information are available, as long as the historical yield increases significantly.

Remotely Sensed Rice Yield Prediction Using Multi-Temporal NDVI Data Derived from NOAA's-AVHRR

PubMed Central

Huang, Jingfeng; Wang, Xiuzhen; Li, Xinxing; Tian, Hanqin; Pan, Zhuokun

2013-01-01

Grain-yield prediction using remotely sensed data have been intensively studied in wheat and maize, but such information is limited in rice, barley, oats and soybeans. The present study proposes a new framework for rice-yield prediction, which eliminates the influence of the technology development, fertilizer application, and management improvement and can be used for the development and implementation of provincial rice-yield predictions. The technique requires the collection of remotely sensed data over an adequate time frame and a corresponding record of the region's crop yields. Longer normalized-difference-vegetation-index (NDVI) time series are preferable to shorter ones for the purposes of rice-yield prediction because the well-contrasted seasons in a longer time series provide the opportunity to build regression models with a wide application range. A regression analysis of the yield versus the year indicated an annual gain in the rice yield of 50 to 128 kg ha−1. Stepwise regression models for the remotely sensed rice-yield predictions have been developed for five typical rice-growing provinces in China. The prediction models for the remotely sensed rice yield indicated that the influences of the NDVIs on the rice yield were always positive. The association between the predicted and observed rice yields was highly significant without obvious outliers from 1982 to 2004. Independent validation found that the overall relative error is approximately 5.82%, and a majority of the relative errors were less than 5% in 2005 and 2006, depending on the study area. The proposed models can be used in an operational context to predict rice yields at the provincial level in China. The methodologies described in the present paper can be applied to any crop for which a sufficient time series of NDVI data and the corresponding historical yield information are available, as long as the historical yield increases significantly. PMID:23967112

Future Applications of Remote Sensing to Archeological Research

NASA Technical Reports Server (NTRS)

Sever, Thomas L.

2003-01-01

Archeology was one of the first disciplines to use aerial photography in its investigations at the turn of the 20th century. However, the low resolution of satellite technology that became available in the 1970 s limited their application to regional studies. That has recently changed. The arrival of the high resolution, multi-spectral capabilities of the IKONOS and QUICKBIRD satellites and the scheduled launch of new satellites in the next few years provides an unlimited horizon for future archeological research. In addition, affordable aerial and ground-based remote sensing instrumentation are providing archeologists with information that is not available through traditional methodologies. Although many archeologists are not yet comfortable with remote sensing technology a new generation has embraced it and is accumulating a wealth of new evidence. They have discovered that through the use of remote sensing it is possible to gather information without disturbing the site and that those cultural resources can be monitored and protected for the future.

Seamline Determination Based on PKGC Segmentation for Remote Sensing Image Mosaicking

PubMed Central

Dong, Qiang; Liu, Jinghong

2017-01-01

This paper presents a novel method of seamline determination for remote sensing image mosaicking. A two-level optimization strategy is applied to determine the seamline. Object-level optimization is executed firstly. Background regions (BRs) and obvious regions (ORs) are extracted based on the results of parametric kernel graph cuts (PKGC) segmentation. The global cost map which consists of color difference, a multi-scale morphological gradient (MSMG) constraint, and texture difference is weighted by BRs. Finally, the seamline is determined in the weighted cost from the start point to the end point. Dijkstra’s shortest path algorithm is adopted for pixel-level optimization to determine the positions of seamline. Meanwhile, a new seamline optimization strategy is proposed for image mosaicking with multi-image overlapping regions. The experimental results show the better performance than the conventional method based on mean-shift segmentation. Seamlines based on the proposed method bypass the obvious objects and take less time in execution. This new method is efficient and superior for seamline determination in remote sensing image mosaicking. PMID:28749446

GMES Initial Operations - Network for Earth Observation Research Training (GIONET)

NASA Astrophysics Data System (ADS)

Nicolas-Perea, V.; Balzter, H.

2012-12-01

GMES Initial Operations - Network for Earth Observation Research Training (GIONET) is a Marie Curie funded project that aims to establish the first of a kind European Centre of Excellence for Earth Observation Research Training. GIONET is a partnership of leading Universities, research institutes and private companies from across Europe aiming to cultivate a community of early stage researchers in the areas of optical and radar remote sensing skilled for the emerging GMES land monitoring services during the GMES Initial Operations period (2011-2013) and beyond. GIONET is expected to satisfy the demand for highly skilled researchers and provide personnel for operational phase of the GMES and monitoring and emergency services. It will achieve this by: -Providing postgraduate training in Earth Observation Science that exposes students to different research disciplines and complementary skills, providing work experiences in the private and academic sectors, and leading to a recognized qualification (Doctorate). -Enabling access to first class training in both fundamental and applied research skills to early-stage researchers at world-class academic centers and market leaders in the private sector. -Building on the experience from previous GMES research and development projects in the land monitoring and emergency information services. The training program through supervised research focuses on 14 research topics (each carried out by an Early Stage Researchers based in one of the partner organization) divided in 5 main areas: Forest monitoring: Global biomass information systems Forest Monitoring of the Congo Basin using Synthetic Aperture radar (SAR) Multi-concept Earth Observation Capabilities for Biomass Mapping and Change Detection: Synergy of Multi-temporal and Multi-frequency Interferometric Radar and Optical Satellite Data Land cover and change: Multi-scale Remote Sensing Synergy for Land Process Studies: from field Spectrometry to Airborne Hyperspectral and Lidar Campaigns to Radar-Optical Satellite Data Multi-temporal, multi-frequency SAR for landscape dynamics Coastal zone and freshwater monitoring: Optical and SAR-based EO in support of Integrated Coastal Zone Management Dynamics and conservation ecology of emergent and submerged macrophytes in Lake Balaton using airborne remote sensing Satellite remote sensing of water quality (chlorophyll and suspended sediment) using MODIS and ship-mounted LIDAR Geohazards and emergency response: Methods for detection and monitoring of small scale land surface feature changes in complex crisis situations Monitoring landslide displacements with Radar Interferometry DINSAR/PSI hybrid methodologies for ground-motion monitoring Climate adaptation and emergency response: Earth Observation based analysis of regional impact of climate change induced water stress patterns fuelling human crisis and conflict situations in semi dry climate regimes Satellite Derived Information for Drought Detection and Estimation of the Water Balance GIONET will also cover methodologies including (i) modelling fundamental radiative processes determining the satellite signal, (ii) atmospheric correction and calibration, (iii) processing higher-order data products, (iii) developing information products from satellite data to meet user requirements, and (iv) statistical methods for assessing the quality and accuracy of data products.

Plant trait detection with multi-scale spectrometry

NASA Astrophysics Data System (ADS)

Gamon, J. A.; Wang, R.

2017-12-01

Proximal and remote sensing using imaging spectrometry offers new opportunities for detecting plant traits, with benefits for phenotyping, productivity estimation, stress detection, and biodiversity studies. Using proximal and airborne spectrometry, we evaluated variation in plant optical properties at various spatial and spectral scales with the goal of identifying optimal scales for distinguishing plant traits related to photosynthetic function. Using directed approaches based on physiological vegetation indices, and statistical approaches based on spectral information content, we explored alternate ways of distinguishing plant traits with imaging spectrometry. With both leaf traits and canopy structure contributing to the signals, results exhibit a strong scale dependence. Our results demonstrate the benefits of multi-scale experimental approaches within a clear conceptual framework when applying remote sensing methods to plant trait detection for phenotyping, productivity, and biodiversity studies.

Cyclic joint remote state preparation in noisy environment

NASA Astrophysics Data System (ADS)

Zhang, Chang-yue; Bai, Ming-qiang; Zhou, Si-qi

2018-06-01

We propose a scheme of cyclic joint remote state preparation for three sides, which takes advantage of three GHZ states to compose product state as quantum channel. Suppose there are six legitimate participants, says Alice, Bob, Charlie, David, Emma and Fred in the scheme. It can be shown that Alice and David can remotely prepare a single-qubit state on Bob's side; meanwhile, Bob and Emma can remotely prepare a desired quantum state on Charlie's side, and Charlie and Fred can also remotely prepare a single-qubit state on Alice's side at the same time. Further, it can be achieved in the opposite direction of the cycle by changing the quantum channel. Based on it, we generalize this protocol to N (N≥3) sides utilizing three multi-qubit GHZ-type states as quantum channel. Therefore, the scheme can achieve cyclic joint remote state preparation, which remotely prepares N states in quantum network with N-party, simultaneously. In addition, we consider that the effect of amplitude-damping noise of the initial states is prepared in four different laboratory. Clearly, we use fidelity to describe how much information has been lost in the cyclic process. Our investigation about the effect of noise shows that the preparing of the initial state in different laboratories will affect the loss of information.

The Increasing Use of Remote Sensing Data in Studying the Climatological Impacts on Public Health

NASA Astrophysics Data System (ADS)

Kempler, S.; Benedict, K. K.; Ceccato, P.; Golden, M.; Maxwell, S.; Morain, S.; Soebiyanto, R.; Tong, D.

2011-12-01

One of the most fortunate outcomes of the capture and transformation of remote sensing data into applied information is their usefulness and impacts to better understanding climatological impacts on public health. Today, with petabytes of remote sensing data providing global coverage of climatological parameters, public health research and policy decision makers have an unprecedented (and growing) data record that relates the effects of climatic parameters, such as rainfall, heat, soil moisture, etc. to incidences and spread of disease, as well as predictive modeling. In addition, tools and services that specifically serve public health researchers and respondents have grown in response to the needs of the these information users. This presentation provides: A perspective of the use of remote sensing data in public health research; NASA funded systems developed to facilitate specific public health decision and public support services, and: Insights on remote sensing data and information services that are available for public health studies and decision making. After providing a review of the use of remote sensing data, the following specific services will be discussed: - Rainfall, Vegetation and Water Bodies Monitoring for Malaria Surveillance - Heat Evaluation and Assessment - Multi-resolution Nested Dust Forecast - Socioeconomic Data and Application Center (SEDAC) Health Related Data and Services - Goddard Earth Sciences Data and Information Services Center (GES DISC) Health Related Data and Services The purpose of this presentation is to provide a (strong) flavor of the data and information services available to public health research and decision making, to invoke new ways of thinking about how public health work can be accomplished, and stimulate new ideas on how information services can be further utilized.

The research of road and vehicle information extraction algorithm based on high resolution remote sensing image

NASA Astrophysics Data System (ADS)

Zhou, Tingting; Gu, Lingjia; Ren, Ruizhi; Cao, Qiong

2016-09-01

With the rapid development of remote sensing technology, the spatial resolution and temporal resolution of satellite imagery also have a huge increase. Meanwhile, High-spatial-resolution images are becoming increasingly popular for commercial applications. The remote sensing image technology has broad application prospects in intelligent traffic. Compared with traditional traffic information collection methods, vehicle information extraction using high-resolution remote sensing image has the advantages of high resolution and wide coverage. This has great guiding significance to urban planning, transportation management, travel route choice and so on. Firstly, this paper preprocessed the acquired high-resolution multi-spectral and panchromatic remote sensing images. After that, on the one hand, in order to get the optimal thresholding for image segmentation, histogram equalization and linear enhancement technologies were applied into the preprocessing results. On the other hand, considering distribution characteristics of road, the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used to suppress water and vegetation information of preprocessing results. Then, the above two processing result were combined. Finally, the geometric characteristics were used to completed road information extraction. The road vector extracted was used to limit the target vehicle area. Target vehicle extraction was divided into bright vehicles extraction and dark vehicles extraction. Eventually, the extraction results of the two kinds of vehicles were combined to get the final results. The experiment results demonstrated that the proposed algorithm has a high precision for the vehicle information extraction for different high resolution remote sensing images. Among these results, the average fault detection rate was about 5.36%, the average residual rate was about 13.60% and the average accuracy was approximately 91.26%.

Smart Cards for Transit : Multi-Use Remotely Interrogated Stored-Data Cards for Fare and Toll Payment

DOT National Transportation Integrated Search

1995-04-01

This project developed relevant information on existing and future, stored readable/writable data card technology for fare and toll payments. The project supports the FTA objective of developing a plan for a common standard card-based fare payment sy...

Experimental Optimization Methods for Multi-Element Airfoils

NASA Technical Reports Server (NTRS)

Landman, Drew; Britcher, Colin P.

1996-01-01

A modern three element airfoil model with a remotely activated flap was used to investigate optimum flap testing position using an automated optimization algorithm in wind tunnel tests. Detailed results for lift coefficient versus flap vertical and horizontal position are presented for two angles of attack: 8 and 14 degrees. An on-line first order optimizer is demonstrated which automatically seeks the optimum lift as a function of flap position. Future work with off-line optimization techniques is introduced and aerodynamic hysteresis effects due to flap movement with flow on are discussed.

Practical single-photon-assisted remote state preparation with non-maximally entanglement

NASA Astrophysics Data System (ADS)

Wang, Dong; Huang, Ai-Jun; Sun, Wen-Yang; Shi, Jia-Dong; Ye, Liu

2016-08-01

Remote state preparation (RSP) and joint remote state preparation (JRSP) protocols for single-photon states are investigated via linear optical elements with partially entangled states. In our scheme, by choosing two-mode instances from a polarizing beam splitter, only the sender in the communication protocol needs to prepare an ancillary single-photon and operate the entanglement preparation process in order to retrieve an arbitrary single-photon state from a photon pair in partially entangled state. In the case of JRSP, i.e., a canonical model of RSP with multi-party, we consider that the information of the desired state is split into many subsets and in prior maintained by spatially separate parties. Specifically, with the assistance of a single-photon state and a three-photon entangled state, it turns out that an arbitrary single-photon state can be jointly and remotely prepared with certain probability, which is characterized by the coefficients of both the employed entangled state and the target state. Remarkably, our protocol is readily to extend to the case for RSP and JRSP of mixed states with the all optical means. Therefore, our protocol is promising for communicating among optics-based multi-node quantum networks.

A fully automatic tool to perform accurate flood mapping by merging remote sensing imagery and ancillary data

NASA Astrophysics Data System (ADS)

D'Addabbo, Annarita; Refice, Alberto; Lovergine, Francesco; Pasquariello, Guido

2016-04-01

Flooding is one of the most frequent and expansive natural hazard. High-resolution flood mapping is an essential step in the monitoring and prevention of inundation hazard, both to gain insight into the processes involved in the generation of flooding events, and from the practical point of view of the precise assessment of inundated areas. Remote sensing data are recognized to be useful in this respect, thanks to the high resolution and regular revisit schedules of state-of-the-art satellites, moreover offering a synoptic overview of the extent of flooding. In particular, Synthetic Aperture Radar (SAR) data present several favorable characteristics for flood mapping, such as their relative insensitivity to the meteorological conditions during acquisitions, as well as the possibility of acquiring independently of solar illumination, thanks to the active nature of the radar sensors [1]. However, flood scenarios are typical examples of complex situations in which different factors have to be considered to provide accurate and robust interpretation of the situation on the ground: the presence of many land cover types, each one with a particular signature in presence of flood, requires modelling the behavior of different objects in the scene in order to associate them to flood or no flood conditions [2]. Generally, the fusion of multi-temporal, multi-sensor, multi-resolution and/or multi-platform Earth observation image data, together with other ancillary information, seems to have a key role in the pursuit of a consistent interpretation of complex scenes. In the case of flooding, distance from the river, terrain elevation, hydrologic information or some combination thereof can add useful information to remote sensing data. Suitable methods, able to manage and merge different kind of data, are so particularly needed. In this work, a fully automatic tool, based on Bayesian Networks (BNs) [3] and able to perform data fusion, is presented. It supplies flood maps describing the dynamics of each analysed event, combining time series of images, acquired by different sensors, with ancillary information. Some experiments have been performed by combining multi-temporal SAR intensity images, InSAR coherence and optical data, with geomorphic and other ground information. The tool has been tested on different flood events occurred in the Basilicata region (Italy) during the last years, showing good capabilities of identification of a large area interested by the flood phenomenon, partially overcoming the obstacle constituted by the presence of scattering/coherence classes corresponding to different land cover types, which respond differently to the presence of water and to inundation evolution [1] A. Refice et al, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 7, pp. 2711-2722, 2014. [2] L. Pulvirenti et al., IEEE Trans. Geosci. Rem. Sens., Vol. PP, pp. 1- 13, 2015. [3] A. D'Addabbo et al., "A Bayesian Network for Flood Detection combining SAR Imagery and Ancillary Data," IEEE Trans. Geosci. Rem. Sens., in press.

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.

Tasseled cap transformation for HJ multispectral remote sensing data

NASA Astrophysics Data System (ADS)

Han, Ling; Han, Xiaoyong

2015-12-01

The tasseled cap transformation of remote sensing data has been widely used in environment, agriculture, forest and ecology. Tasseled cap transformation coefficients matrix of HJ multi-spectrum data has been established through Givens rotation matrix to rotate principal component transform vector to whiteness, greenness and blueness direction of ground object basing on 24 scenes year-round HJ multispectral remote sensing data. The whiteness component enhances the brightness difference of ground object, and the greenness component preserves more detailed information of vegetation change while enhances the vegetation characteristic, and the blueness component significantly enhances factory with blue plastic house roof around the town and also can enhance brightness of water. Tasseled cap transformation coefficients matrix of HJ will enhance the application effect of HJ multispectral remote sensing data in their application fields.

Informing a hydrological model of the Ogooué with multi-mission remote sensing data

NASA Astrophysics Data System (ADS)

Kittel, Cecile; Bauer-Gottwein, Peter; Nielsen, Karina; Tøttrup, Christian

2017-04-01

Knowledge on hydrological regimes of river basins is crucial for water management. However, data requirements often limit the applicability of hydrological models in basins with scarce in-situ data. Remote sensing provides a unique possibility to acquire information on hydrological variables in these basins. This study explores how multi-mission remote sensing data can inform a hydrological model. The Ogooué basin in Gabon is used as study area. No previous modelling efforts have been conducted for the basin and only historical flow and precipitation observations are available. Publicly available remote sensing observations are used to parametrize, force, calibrate and validate a hydrological model of the Ogooué. The modelling framework used in the study, is a lumped conceptual rainfall-runoff model based on the Budyko framework coupled to a Muskingum routing scheme. Precipitation is a crucial driver of the land-surface water balance, therefore two satellite-based rainfall estimates, Tropical Rainfall Measuring Mission (TRMM) product 3B42 version 7 and Famine Early Warning System - Rainfall Estimate (FEWS-RFE), are compared. The comparison shows good seasonal and spatial agreement between the products; however, TRMM consistently predicts significantly more precipitation: 1726 mm on average per year against 1556 mm for FEWS-RFE. Best modeling results are obtained with the TRMM precipitation forcing. Model calibration combines historical in-situ flow observations and GRACE total water storage observations using the Jet Propulsion Laboratory (JPL) mascon solution in a multi-objective approach. The two models are calibrated using flow duration curves and climatology benchmarks to overcome the lack of simultaneity between simulated and observed discharge. The objectives are aggregated into a global objective function, and the models are calibrated using the Shuffled Complex Evolution Algorithm. Water height observations from drifting orbit altimetry missions are extracted along the river line, using a detailed water mask based on Sentinel-1 SAR imagery. 1399 single CryoSat-2 altimetry observations and 48 ICESat observations are acquired. Additionally, water heights have been measured by the repeat-orbit satellite missions Envisat and Jason-2 at 12 virtual stations along the river. The four missions show generally good agreement in terms of mean annual water height amplitudes. The altimetry observations are used to validate the hydrological model of the Ogooué River. By combining hydrological modelling and remote sensing, new information on an otherwise unstudied basin is obtained. The study shows the potential of using remote sensing observations to parameterize, force, calibrate and validate models of poorly gauged river basins. Specifically, the study shows how Sentinel-1 SAR imagery supports the extraction of satellite altimetry data over rivers. The model can be used to assess climate change scenarios, evaluate hydraulic infrastructure development projects and predict the impact of irrigation diversions.

[Estimating medicinal yield of Seutellaria baicalensis in Beijing-Tianjin-Hebei region based on 3S technology].

PubMed

Liu, Jin-xinp; Lu, Heng; Zeng, Yan; Yue, Jian-wei; Meng, Fan-yun; Zhang, Yi-guang

2012-09-01

Resources survey of traditional Chinese medicine and reserves estimation are found to be the most important issues for the protection and utilization of traditional Chinese medicine resources, this paper used multi-spatial resolution remote sensing images (RS) , geographic information systems (GIS) and global positioning system (GPS) , to establish Scutellaria resources survey of 3S data platform. Combined with the traditional field survey methods, small-scale habitat types were established based on different skullcap reserve estimation model, which can estimate reserves of the wild Scutellaria in Beijing-Tianjin-Hebei region and improve the estimation accuracy. It can provide an important parameter for the fourth national survey of traditional Chinese medicine resources and traditional Chinese medicine reserves estimates based on 3S technology by multiple spatial scales model.

A modified approach combining FNEA and watershed algorithms for segmenting remotely-sensed optical images

NASA Astrophysics Data System (ADS)

Liu, Likun

2018-01-01

In the field of remote sensing image processing, remote sensing image segmentation is a preliminary step for later analysis of remote sensing image processing and semi-auto human interpretation, fully-automatic machine recognition and learning. Since 2000, a technique of object-oriented remote sensing image processing method and its basic thought prevails. The core of the approach is Fractal Net Evolution Approach (FNEA) multi-scale segmentation algorithm. The paper is intent on the research and improvement of the algorithm, which analyzes present segmentation algorithms and selects optimum watershed algorithm as an initialization. Meanwhile, the algorithm is modified by modifying an area parameter, and then combining area parameter with a heterogeneous parameter further. After that, several experiments is carried on to prove the modified FNEA algorithm, compared with traditional pixel-based method (FCM algorithm based on neighborhood information) and combination of FNEA and watershed, has a better segmentation result.

The potential of volunteered geographic information to investigate peri-urbanization in the conservation zone of Mexico City.

PubMed

Heider, Katharina; Lopez, Juan Miguel Rodriguez; Scheffran, Jürgen

2018-03-14

Due to the availability of Web 2.0 technologies, volunteered geographic information (VGI) is on the rise. This new type of data is available on many topics and on different scales. Thus, it has become interesting for research. This article deals with the collective potential of VGI and remote sensing to detect peri-urbanization in the conservation zone of Mexico City. On the one hand, remote sensing identifies horizontal urban expansion, and on the other hand, VGI of ecological complaints provides data about informal settlements. This enables the combination of top-down approaches (remote sensing) and bottom-up approaches (ecological complaints). Within the analysis, we identify areas of high urbanization as well as complaint densities and bring them together in a multi-scale analysis using Geographic Information Systems (GIS). Furthermore, we investigate the influence of settlement patterns and main roads on the peri-urbanization process in Mexico City using OpenStreetMap. Peri-urbanization is detected especially in the transition zone between the urban and rural (conservation) area and near main roads as well as settlements.

Application of remote sensing to estimating soil erosion potential

NASA Technical Reports Server (NTRS)

Morris-Jones, D. R.; Kiefer, R. W.

1980-01-01

A variety of remote sensing data sources and interpretation techniques has been tested in a 6136 hectare watershed with agricultural, forest and urban land cover to determine the relative utility of alternative aerial photographic data sources for gathering the desired land use/land cover data. The principal photographic data sources are high altitude 9 x 9 inch color infrared photos at 1:120,000 and 1:60,000 and multi-date medium altitude color and color infrared photos at 1:60,000. Principal data for estimating soil erosion potential include precipitation, soil, slope, crop, crop practice, and land use/land cover data derived from topographic maps, soil maps, and remote sensing. A computer-based geographic information system organized on a one-hectare grid cell basis is used to store and quantify the information collected using different data sources and interpretation techniques. Research results are compared with traditional Universal Soil Loss Equation field survey methods.

The Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX)-a synopsis

USDA-ARS?s Scientific Manuscript database

Considering California’s recent multi-year drought as well as the severe droughts recently in Italy and South Africa, there is a critical need for accurate and timely evapotranspiration (ET) and crop stress information to ensure long-term sustainability of high-value value perennial crops (vineyards...

Dangerous gas detection based on infrared video

NASA Astrophysics Data System (ADS)

Ding, Kang; Hong, Hanyu; Huang, Likun

2018-03-01

As the gas leak infrared imaging detection technology has significant advantages of high efficiency and remote imaging detection, in order to enhance the detail perception of observers and equivalently improve the detection limit, we propose a new type of gas leak infrared image detection method, which combines background difference methods and multi-frame interval difference method. Compared to the traditional frame methods, the multi-frame interval difference method we proposed can extract a more complete target image. By fusing the background difference image and the multi-frame interval difference image, we can accumulate the information of infrared target image of the gas leak in many aspect. The experiment demonstrate that the completeness of the gas leakage trace information is enhanced significantly, and the real-time detection effect can be achieved.

Informing a hydrological model of the Ogooué with multi-mission remote sensing data

NASA Astrophysics Data System (ADS)

Kittel, Cecile M. M.; Nielsen, Karina; Tøttrup, Christian; Bauer-Gottwein, Peter

2018-02-01

Remote sensing provides a unique opportunity to inform and constrain a hydrological model and to increase its value as a decision-support tool. In this study, we applied a multi-mission approach to force, calibrate and validate a hydrological model of the ungauged Ogooué river basin in Africa with publicly available and free remote sensing observations. We used a rainfall-runoff model based on the Budyko framework coupled with a Muskingum routing approach. We parametrized the model using the Shuttle Radar Topography Mission digital elevation model (SRTM DEM) and forced it using precipitation from two satellite-based rainfall estimates, FEWS-RFE (Famine Early Warning System rainfall estimate) and the Tropical Rainfall Measuring Mission (TRMM) 3B42 v.7, and temperature from ECMWF ERA-Interim. We combined three different datasets to calibrate the model using an aggregated objective function with contributions from (1) historical in situ discharge observations from the period 1953-1984 at six locations in the basin, (2) radar altimetry measurements of river stages by Envisat and Jason-2 at 12 locations in the basin and (3) GRACE (Gravity Recovery and Climate Experiment) total water storage change (TWSC). Additionally, we extracted CryoSat-2 observations throughout the basin using a Sentinel-1 SAR (synthetic aperture radar) imagery water mask and used the observations for validation of the model. The use of new satellite missions, including Sentinel-1 and CryoSat-2, increased the spatial characterization of river stage. Throughout the basin, we achieved good agreement between observed and simulated discharge and the river stage, with an RMSD between simulated and observed water amplitudes at virtual stations of 0.74 m for the TRMM-forced model and 0.87 m for the FEWS-RFE-forced model. The hydrological model also captures overall total water storage change patterns, although the amplitude of storage change is generally underestimated. By combining hydrological modeling with multi-mission remote sensing from 10 different satellite missions, we obtain new information on an otherwise unstudied basin. The proposed model is the best current baseline characterization of hydrological conditions in the Ogooué in light of the available observations.

Unsupervised Change Detection for Geological and Ecological Monitoring via Remote Sensing: Application on a Volcanic Area

NASA Astrophysics Data System (ADS)

Falco, N.; Pedersen, G. B. M.; Vilmunandardóttir, O. K.; Belart, J. M. M. C.; Sigurmundsson, F. S.; Benediktsson, J. A.

2016-12-01

The project "Environmental Mapping and Monitoring of Iceland by Remote Sensing (EMMIRS)" aims at providing fast and reliable mapping and monitoring techniques on a big spatial scale with a high temporal resolution of the Icelandic landscape. Such mapping and monitoring will be crucial to both mitigate and understand the scale of processes and their often complex interlinked feedback mechanisms.In the EMMIRS project, the Hekla volcano area is one of the main sites under study, where the volcanic eruptions, extreme weather and human activities had an extensive impact on the landscape degradation. The development of innovative remote sensing approaches to compute earth observation variables as automatically as possible is one of the main tasks of the EMMIRS project. Furthermore, a temporal remote sensing archive is created and composed by images acquired by different sensors (Landsat, RapidEye, ASTER and SPOT5). Moreover, historical aerial stereo photos allowed decadal reconstruction of the landscape by reconstruction of digital elevation models. Here, we propose a novel architecture for automatic unsupervised change detection analysis able to ingest multi-source data in order to detect landscape changes in the Hekla area. The change detection analysis is based on multi-scale analysis, which allows the identification of changes at different level of abstraction, from pixel-level to region-level. For this purpose, operators defined in mathematical morphology framework are implemented to model the contextual information, represented by the neighbour system of a pixel, allowing the identification of changes related to both geometrical and spectral domains. Automatic radiometric normalization strategy is also implemented as pre-processing step, aiming at minimizing the effect of different acquisition conditions. The proposed architecture is tested on multi-temporal data sets acquired over different time periods coinciding with the last three eruptions (1980-1981, 1991, 2000) occurred on Hekla volcano. The results reveal emplacement of new lava flows and the initial vegetation succession, providing insightful information on the evolving of vegetation in such environment. Shadow and snow patch changes are resolved in post-processing by exploiting the available spectral information.

Bush Encroachment Mapping for Africa - Multi-Scale Analysis with Remote Sensing and GIS

NASA Astrophysics Data System (ADS)

Graw, V. A. M.; Oldenburg, C.; Dubovyk, O.

2015-12-01

Bush encroachment describes a global problem which is especially facing the savanna ecosystem in Africa. Livestock is directly affected by decreasing grasslands and inedible invasive species which defines the process of bush encroachment. For many small scale farmers in developing countries livestock represents a type of insurance in times of crop failure or drought. Among that bush encroachment is also a problem for crop production. Studies on the mapping of bush encroachment so far focus on small scales using high-resolution data and rarely provide information beyond the national level. Therefore a process chain was developed using a multi-scale approach to detect bush encroachment for whole Africa. The bush encroachment map is calibrated with ground truth data provided by experts in Southern, Eastern and Western Africa. By up-scaling location specific information on different levels of remote sensing imagery - 30m with Landsat images and 250m with MODIS data - a map is created showing potential and actual areas of bush encroachment on the African continent and thereby provides an innovative approach to map bush encroachment on the regional scale. A classification approach links location data based on GPS information from experts to the respective pixel in the remote sensing imagery. Supervised classification is used while actual bush encroachment information represents the training samples for the up-scaling. The classification technique is based on Random Forests and regression trees, a machine learning classification approach. Working on multiple scales and with the help of field data an innovative approach can be presented showing areas affected by bush encroachment on the African continent. This information can help to prevent further grassland decrease and identify those regions where land management strategies are of high importance to sustain livestock keeping and thereby also secure livelihoods in rural areas.

Arctic Collaborative Environment: A New Multi-National Partnership for Arctic Science and Decision Support

NASA Technical Reports Server (NTRS)

Laymon, Charles A,; Kress, Martin P.; McCracken, Jeff E.; Spehn, Stephen L.; Tanner, Steve

2011-01-01

The Arctic Collaborative Environment (ACE) project is a new international partnership for information sharing to meet the challenges of addressing Arctic. The goal of ACE is to create an open source, web-based, multi-national monitoring, analysis, and visualization decision-support system for Arctic environmental assessment, management, and sustainability. This paper will describe the concept, system architecture, and data products that are being developed and disseminated among partners and independent users through remote access.

Improving simulations of snow water equivalent and total water storage changes over the Upper Yangtze River basin using multi-source remote sensing data

NASA Astrophysics Data System (ADS)

Han, P.; Long, D.

2017-12-01

Snow water equivalent (SWE) and total water storage (TWS) changes are important hydrological state variables over cryospheric regions, such as China's Upper Yangtze River (UYR) basin. Accurate simulation of these two state variables plays a critical role in understanding hydrological processes over this region and, in turn, benefits water resource management, hydropower development, and ecological integrity over the lower reaches of the Yangtze River, one of the largest rivers globally. In this study, an improved CREST model coupled with a snow and glacier melting module was used to simulate SWE and TWS changes over the UYR, and to quantify contributions of snow and glacier meltwater to the total runoff. Forcing, calibration, and validation data are mainly from multi-source remote sensing observations, including satellite-based precipitation estimates, passive microwave remote sensing-based SWE, and GRACE-derived TWS changes, along with streamflow measurements at the Zhimenda gauging station. Results show that multi-source remote sensing information can be extremely valuable in model forcing, calibration, and validation over the poorly gauged region. The simulated SWE and TWS changes and the observed counterparts are highly consistent, showing NSE coefficients higher than 0.8. The results also show that the contributions of snow and glacier meltwater to the total runoff are 8% and 6%, respectively, during the period 2003‒2014, which is an important source of runoff. Moreover, from this study, the TWS is found to increase at a rate of 5 mm/a ( 0.72 Gt/a) for the period 2003‒2014. The snow melting module may overestimate SWE for high precipitation events and was improved in this study. Key words: CREST model; Remote Sensing; Melting model; Source Region of the Yangtze River

Multi-scale functional mapping of tidal marsh vegetation for restoration monitoring

NASA Astrophysics Data System (ADS)

Tuxen Bettman, Karin

2007-12-01

Nearly half of the world's natural wetlands have been destroyed or degraded, and in recent years, there have been significant endeavors to restore wetland habitat throughout the world. Detailed mapping of restoring wetlands can offer valuable information about changes in vegetation and geomorphology, which can inform the restoration process and ultimately help to improve chances of restoration success. I studied six tidal marshes in the San Francisco Estuary, CA, US, between 2003 and 2004 in order to develop techniques for mapping tidal marshes at multiple scales by incorporating specific restoration objectives for improved longer term monitoring. I explored a "pixel-based" remote sensing image analysis method for mapping vegetation in restored and natural tidal marshes, describing the benefits and limitations of this type of approach (Chapter 2). I also performed a multi-scale analysis of vegetation pattern metrics for a recently restored tidal marsh in order to target the metrics that are consistent across scales and will be robust measures of marsh vegetation change (Chapter 3). Finally, I performed an "object-based" image analysis using the same remotely sensed imagery, which maps vegetation type and specific wetland functions at multiple scales (Chapter 4). The combined results of my work highlight important trends and management implications for monitoring wetland restoration using remote sensing, and will better enable restoration ecologists to use remote sensing for tidal marsh monitoring. Several findings important for tidal marsh restoration monitoring were made. Overall results showed that pixel-based methods are effective at quantifying landscape changes in composition and diversity in recently restored marshes, but are limited in their use for quantifying smaller, more fine-scale changes. While pattern metrics can highlight small but important changes in vegetation composition and configuration across years, scientists should exercise caution when using metrics in their studies or to validate restoration management decisions, and multi-scale analyses should be performed before metrics are used in restoration science for important management decisions. Lastly, restoration objectives, ecosystem function, and scale can each be integrated into monitoring techniques using remote sensing for improved restoration monitoring.

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.

Design and engineering analysis of material procurement mobile operation platform

NASA Astrophysics Data System (ADS)

Ding, H.; Li, J.

2014-03-01

The material procurement mobile operation platform (MPMOP) consists of six modules, including network operation, truck transportation, remote communication, satellite positioning, power supply and environment regulation. The MPMOP is designed to have six major functions, including online procurement, command control, remote communication, satellite positioning, information management and auxiliary decision. The paper implements an engineering analysis on the MPMOP from three aspects, including transportation transfinite, centroid, and power dissipation.

Integrated Decision Tools for Sustainable Watershed/Ground Water and Crop Health using Predictive Weather, Remote Sensing, and Irrigation Decision Tools

NASA Astrophysics Data System (ADS)

Jones, A. S.; Andales, A.; McGovern, C.; Smith, G. E. B.; David, O.; Fletcher, S. J.

2017-12-01

US agricultural and Govt. lands have a unique co-dependent relationship, particularly in the Western US. More than 30% of all irrigated US agricultural output comes from lands sustained by the Ogallala Aquifer in the western Great Plains. Six US Forest Service National Grasslands reside within the aquifer region, consisting of over 375,000 ha (3,759 km2) of USFS managed lands. Likewise, National Forest lands are the headwaters to many intensive agricultural regions. Our Ogallala Aquifer team is enhancing crop irrigation decision tools with predictive weather and remote sensing data to better manage water for irrigated crops within these regions. An integrated multi-model software framework is used to link irrigation decision tools, resulting in positive management benefits on natural water resources. Teams and teams-of-teams can build upon these multi-disciplinary multi-faceted modeling capabilities. For example, the CSU Catalyst for Innovative Partnerships program has formed a new multidisciplinary team that will address "Rural Wealth Creation" focusing on the many integrated links between economic, agricultural production and management, natural resource availabilities, and key social aspects of govt. policy recommendations. By enhancing tools like these with predictive weather and other related data (like in situ measurements, hydrologic models, remotely sensed data sets, and (in the near future) linking to agro-economic and life cycle assessment models) this work demonstrates an integrated data-driven future vision of inter-meshed dynamic systems that can address challenging multi-system problems. We will present the present state of the work and opportunities for future involvement.

Does remote sensing help translating local SGD investigation to large spatial scales?

NASA Astrophysics Data System (ADS)

Moosdorf, N.; Mallast, U.; Hennig, H.; Schubert, M.; Knoeller, K.; Neehaul, Y.

2016-02-01

Within the last 20 years, studies on submarine groundwater discharge (SGD) have revealed numerous processes, temporal behavior and quantitative estimations as well as best-practice and localization methods. This plethora on information is valuable regarding the understanding of magnitude and effects of SGD for the respective location. Yet, since given local conditions vary, the translation of local understanding, magnitudes and effects to a regional or global scale is not trivial. In contrast, modeling approaches (e.g. 228Ra budget) tackling SGD on a global scale do provide quantitative global estimates but have not been related to local investigations. This gap between the two approaches, local and global, and the combination and/or translation of either one to the other represents one of the mayor challenges the SGD community currently faces. But what if remote sensing can provide certain information that may be used as translation between the two, similar to transfer functions in many other disciplines allowing an extrapolation from in-situ investigated and quantified SGD (discrete information) to regional scales or beyond? Admittedly, the sketched future is ambitious and we will certainly not be able to present a solution to the raised question. Nonetheless, we will show a remote sensing based approach that is already able to identify potential SGD sites independent on location or hydrogeological conditions. Based on multi-temporal thermal information of the water surface as core of the approach, SGD influenced sites display a smaller thermal variation (thermal anomalies) than surrounding uninfluenced areas. Despite the apparent simplicity, the automatized approach has helped to localize several sites that could be validated with proven in-situ methods. At the same time it embodies the risk to identify false positives that can only be avoided if we can `calibrate' the so obtained thermal anomalies to in-situ data. We will present all pros and cons of our approach with the intention to contribute to the solution of translating SGD investigation to larger scales.

Research of building information extraction and evaluation based on high-resolution remote-sensing imagery

NASA Astrophysics Data System (ADS)

Cao, Qiong; Gu, Lingjia; Ren, Ruizhi; Wang, Lang

2016-09-01

Building extraction currently is important in the application of high-resolution remote sensing imagery. At present, quite a few algorithms are available for detecting building information, however, most of them still have some obvious disadvantages, such as the ignorance of spectral information, the contradiction between extraction rate and extraction accuracy. The purpose of this research is to develop an effective method to detect building information for Chinese GF-1 data. Firstly, the image preprocessing technique is used to normalize the image and image enhancement is used to highlight the useful information in the image. Secondly, multi-spectral information is analyzed. Subsequently, an improved morphological building index (IMBI) based on remote sensing imagery is proposed to get the candidate building objects. Furthermore, in order to refine building objects and further remove false objects, the post-processing (e.g., the shape features, the vegetation index and the water index) is employed. To validate the effectiveness of the proposed algorithm, the omission errors (OE), commission errors (CE), the overall accuracy (OA) and Kappa are used at final. The proposed method can not only effectively use spectral information and other basic features, but also avoid extracting excessive interference details from high-resolution remote sensing images. Compared to the original MBI algorithm, the proposed method reduces the OE by 33.14% .At the same time, the Kappa increase by 16.09%. In experiments, IMBI achieved satisfactory results and outperformed other algorithms in terms of both accuracies and visual inspection

S.I.I.A for monitoring crop evolution and anomaly detection in Andalusia by remote sensing

NASA Astrophysics Data System (ADS)

Rodriguez Perez, Antonio Jose; Louakfaoui, El Mostafa; Munoz Rastrero, Antonio; Rubio Perez, Luis Alberto; de Pablos Epalza, Carmen

2004-02-01

A new remote sensing application was developed and incorporated to the Agrarian Integrated Information System (S.I.I.A), project which is involved on integrating the regional farming databases from a geographical point of view, adding new values and uses to the original information. The project is supported by the Studies and Statistical Service, Regional Government Ministry of Agriculture and Fisheries (CAP). The process integrates NDVI values from daily NOAA-AVHRR and monthly IRS-WIFS images, and crop classes location maps. Agrarian local information and meteorological information is being included in the working process to produce a synergistic effect. An updated crop-growing evaluation state is obtained by 10-days periods, crop class, sensor type (including data fusion) and administrative geographical borders. Last ten years crop database (1992-2002) has been organized according to these variables. Crop class database can be accessed by an application which helps users on the crop statistical analysis. Multi-temporal and multi-geographical comparative analysis can be done by the user, not only for a year but also for a historical point of view. Moreover, real time crop anomalies can be detected and analyzed. Most of the output products will be available on Internet in the near future by a on-line application.

Environmental mapping and monitoring of Iceland by remote sensing (EMMIRS)

NASA Astrophysics Data System (ADS)

Pedersen, Gro B. M.; Vilmundardóttir, Olga K.; Falco, Nicola; Sigurmundsson, Friðþór S.; Rustowicz, Rose; Belart, Joaquin M.-C.; Gísladóttir, Gudrun; Benediktsson, Jón A.

2016-04-01

Iceland is exposed to rapid and dynamic landscape changes caused by natural processes and man-made activities, which impact and challenge the country. Fast and reliable mapping and monitoring techniques are needed on a big spatial scale. However, currently there is lack of operational advanced information processing techniques, which are needed for end-users to incorporate remote sensing (RS) data from multiple data sources. Hence, the full potential of the recent RS data explosion is not being fully exploited. The project Environmental Mapping and Monitoring of Iceland by Remote Sensing (EMMIRS) bridges the gap between advanced information processing capabilities and end-user mapping of the Icelandic environment. This is done by a multidisciplinary assessment of two selected remote sensing super sites, Hekla and Öræfajökull, which encompass many of the rapid natural and man-made landscape changes that Iceland is exposed to. An open-access benchmark repository of the two remote sensing supersites is under construction, providing high-resolution LIDAR topography and hyperspectral data for land-cover and landform classification. Furthermore, a multi-temporal and multi-source archive stretching back to 1945 allows a decadal evaluation of landscape and ecological changes for the two remote sensing super sites by the development of automated change detection techniques. The development of innovative pattern recognition and machine learning-based approaches to image classification and change detection is one of the main tasks of the EMMIRS project, aiming to extract and compute earth observation variables as automatically as possible. Ground reference data collected through a field campaign will be used to validate the implemented methods, which outputs are then inferred with geological and vegetation models. Here, preliminary results of an automatic land-cover classification based on hyperspectral image analysis are reported. Furthermore, the EMMIRS project investigates the complex landscape dynamics between geological and ecological processes. This is done through cross-correlation of mapping results and implementation of modelling techniques that simulate geological and ecological processes in order to extrapolate the landscape evolution

Faithful teleportation of multi-particle states involving multi spatially remote agents via probabilistic channels

NASA Astrophysics Data System (ADS)

Jiang, Min; Li, Hui; Zhang, Zeng-ke; Zeng, Jia

2011-02-01

We present an approach to faithfully teleport an unknown quantum state of entangled particles in a multi-particle system involving multi spatially remote agents via probabilistic channels. In our scheme, the integrity of an entangled multi-particle state can be maintained even when the construction of a faithful channel fails. Furthermore, in a quantum teleportation network, there are generally multi spatially remote agents which play the role of relay nodes between a sender and a distant receiver. Hence, we propose two schemes for directly and indirectly constructing a faithful channel between the sender and the distant receiver with the assistance of relay agents, respectively. Our results show that the required auxiliary particle resources, local operations and classical communications are considerably reduced for the present purpose.

Development and Control of Multi-Degree-of-Freedom Mobile Robot for Acquisition of Road Environmental Modes

NASA Astrophysics Data System (ADS)

Murata, Naoya; Katsura, Seiichiro

Acquisition of information about the environment around a mobile robot is important for purposes such as controlling the robot from a remote location and in situations such as that when the robot is running autonomously. In many researches, audiovisual information is used. However, acquisition of information about force sensation, which is included in environmental information, has not been well researched. The mobile-hapto, which is a remote control system with force information, has been proposed, but the robot used for the system can acquire only the horizontal component of forces. For this reason, in this research, a three-wheeled mobile robot that consists of seven actuators was developed and its control system was constructed. It can get information on horizontal and vertical forces without using force sensors. By using this robot, detailed information on the forces in the environment can be acquired and the operability of the robot and its capability to adjust to the environment are expected to improve.

Analysing and correcting the differences between multi-source and multi-scale spatial remote sensing observations.

PubMed

Dong, Yingying; Luo, Ruisen; Feng, Haikuan; Wang, Jihua; Zhao, Jinling; Zhu, Yining; Yang, Guijun

2014-01-01

Differences exist among analysis results of agriculture monitoring and crop production based on remote sensing observations, which are obtained at different spatial scales from multiple remote sensors in same time period, and processed by same algorithms, models or methods. These differences can be mainly quantitatively described from three aspects, i.e. multiple remote sensing observations, crop parameters estimation models, and spatial scale effects of surface parameters. Our research proposed a new method to analyse and correct the differences between multi-source and multi-scale spatial remote sensing surface reflectance datasets, aiming to provide references for further studies in agricultural application with multiple remotely sensed observations from different sources. The new method was constructed on the basis of physical and mathematical properties of multi-source and multi-scale reflectance datasets. Theories of statistics were involved to extract statistical characteristics of multiple surface reflectance datasets, and further quantitatively analyse spatial variations of these characteristics at multiple spatial scales. Then, taking the surface reflectance at small spatial scale as the baseline data, theories of Gaussian distribution were selected for multiple surface reflectance datasets correction based on the above obtained physical characteristics and mathematical distribution properties, and their spatial variations. This proposed method was verified by two sets of multiple satellite images, which were obtained in two experimental fields located in Inner Mongolia and Beijing, China with different degrees of homogeneity of underlying surfaces. Experimental results indicate that differences of surface reflectance datasets at multiple spatial scales could be effectively corrected over non-homogeneous underlying surfaces, which provide database for further multi-source and multi-scale crop growth monitoring and yield prediction, and their corresponding consistency analysis evaluation.

Analysing and Correcting the Differences between Multi-Source and Multi-Scale Spatial Remote Sensing Observations

PubMed Central

Dong, Yingying; Luo, Ruisen; Feng, Haikuan; Wang, Jihua; Zhao, Jinling; Zhu, Yining; Yang, Guijun

2014-01-01

Differences exist among analysis results of agriculture monitoring and crop production based on remote sensing observations, which are obtained at different spatial scales from multiple remote sensors in same time period, and processed by same algorithms, models or methods. These differences can be mainly quantitatively described from three aspects, i.e. multiple remote sensing observations, crop parameters estimation models, and spatial scale effects of surface parameters. Our research proposed a new method to analyse and correct the differences between multi-source and multi-scale spatial remote sensing surface reflectance datasets, aiming to provide references for further studies in agricultural application with multiple remotely sensed observations from different sources. The new method was constructed on the basis of physical and mathematical properties of multi-source and multi-scale reflectance datasets. Theories of statistics were involved to extract statistical characteristics of multiple surface reflectance datasets, and further quantitatively analyse spatial variations of these characteristics at multiple spatial scales. Then, taking the surface reflectance at small spatial scale as the baseline data, theories of Gaussian distribution were selected for multiple surface reflectance datasets correction based on the above obtained physical characteristics and mathematical distribution properties, and their spatial variations. This proposed method was verified by two sets of multiple satellite images, which were obtained in two experimental fields located in Inner Mongolia and Beijing, China with different degrees of homogeneity of underlying surfaces. Experimental results indicate that differences of surface reflectance datasets at multiple spatial scales could be effectively corrected over non-homogeneous underlying surfaces, which provide database for further multi-source and multi-scale crop growth monitoring and yield prediction, and their corresponding consistency analysis evaluation. PMID:25405760

Propagation Limitations in Remote Sensing.

DTIC Science & Technology

Contents: Multi-sensors and systems in remote sensing ; Radar sensing systems over land; Remote sensing techniques in oceanography; Influence of...propagation media and background; Infrared techniques in remote sensing ; Photography in remote sensing ; Analytical studies in remote sensing .

The Feasibility Evaluation of Land Use Change Detection Using GAOFEN-3 Data

NASA Astrophysics Data System (ADS)

Huang, G.; Sun, Y.; Zhao, Z.

2018-04-01

GaoFen-3 (GF-3) satellite, is the first C band and multi-polarimetric Synthetic Aperture Radar (SAR) satellite in China. In order to explore the feasibility of GF-3 satellite in remote sensing interpretation and land-use remote sensing change detection, taking Guangzhou, China as a study area, the full polarimetric image of GF-3 satellite with 8 m resolution of two temporal as the data source. Firstly, the image is pre-processed by orthorectification, image registration and mosaic, and the land-use remote sensing digital orthophoto map (DOM) in 2017 is made according to the each county. Then the classification analysis and judgment of ground objects on the image are carried out by means of ArcGIS combining with the auxiliary data and using artificial visual interpretation, to determine the area of changes and the category of change objects. According to the unified change information extraction principle to extract change areas. Finally, the change detection results are compared with 3 m resolution TerraSAR-X data and 2 m resolution multi-spectral image, and the accuracy is evaluated. Experimental results show that the accuracy of the GF-3 data is over 75 % in detecting the change of ground objects, and the detection capability of new filling soil is better than that of TerraSAR-X data, verify the detection and monitoring capability of GF-3 data to the change information extraction, also, it shows that GF-3 can provide effective data support for the remote sensing detection of land resources.

Multi-factor challenge/response approach for remote biometric authentication

NASA Astrophysics Data System (ADS)

Al-Assam, Hisham; Jassim, Sabah A.

2011-06-01

Although biometric authentication is perceived to be more reliable than traditional authentication schemes, it becomes vulnerable to many attacks when it comes to remote authentication over open networks and raises serious privacy concerns. This paper proposes a biometric-based challenge-response approach to be used for remote authentication between two parties A and B over open networks. In the proposed approach, a remote authenticator system B (e.g. a bank) challenges its client A who wants to authenticate his/her self to the system by sending a one-time public random challenge. The client A responds by employing the random challenge along with secret information obtained from a password and a token to produce a one-time cancellable representation of his freshly captured biometric sample. The one-time biometric representation, which is based on multi-factor, is then sent back to B for matching. Here, we argue that eavesdropping of the one-time random challenge and/or the resulting one-time biometric representation does not compromise the security of the system, and no information about the original biometric data is leaked. In addition to securing biometric templates, the proposed protocol offers a practical solution for the replay attack on biometric systems. Moreover, we propose a new scheme for generating a password-based pseudo random numbers/permutation to be used as a building block in the proposed approach. The proposed scheme is also designed to provide protection against repudiation. We illustrate the viability and effectiveness of the proposed approach by experimental results based on two biometric modalities: fingerprint and face biometrics.

A land data assimilation system for sub-Saharan Africa food and water security applications

PubMed Central

McNally, Amy; Arsenault, Kristi; Kumar, Sujay; Shukla, Shraddhanand; Peterson, Pete; Wang, Shugong; Funk, Chris; Peters-Lidard, Christa D.; Verdin, James P.

2017-01-01

Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWS NET’s operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa. PMID:28195575

A land data assimilation system for sub-Saharan Africa food and water security applications

USGS Publications Warehouse

McNally, Amy; Arsenault, Kristi; Kumar, Sujay; Shukla, Shraddhanand; Peterson, Pete; Wang, Shugong; Funk, Chris; Peters-Lidard, Christa; Verdin, James

2017-01-01

Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWS NET’s operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa.

A land data assimilation system for sub-Saharan Africa food and water security applications.

PubMed

McNally, Amy; Arsenault, Kristi; Kumar, Sujay; Shukla, Shraddhanand; Peterson, Pete; Wang, Shugong; Funk, Chris; Peters-Lidard, Christa D; Verdin, James P

2017-02-14

Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWS NET's operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa.

Data Descriptor: A Land Data Assimilation System for Sub-Saharan Africa Food and Water Security Applications

NASA Technical Reports Server (NTRS)

McNally, Amy; Arsenault, Krist; Kumar, Sujay; Shukla, Shraddhanand; Peter, Pete; Wang, Shugong; Funk, Chris; Peters-Lidard, Christa D.; Verdin, James

2017-01-01

Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWSNETs operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa.

A land data assimilation system for sub-Saharan Africa food and water security applications

NASA Astrophysics Data System (ADS)

McNally, Amy; Arsenault, Kristi; Kumar, Sujay; Shukla, Shraddhanand; Peterson, Pete; Wang, Shugong; Funk, Chris; Peters-Lidard, Christa D.; Verdin, James P.

2017-02-01

Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWS NET's operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa.

Object Manifold Alignment for Multi-Temporal High Resolution Remote Sensing Images Classification

NASA Astrophysics Data System (ADS)

Gao, G.; Zhang, M.; Gu, Y.

2017-05-01

Multi-temporal remote sensing images classification is very useful for monitoring the land cover changes. Traditional approaches in this field mainly face to limited labelled samples and spectral drift of image information. With spatial resolution improvement, "pepper and salt" appears and classification results will be effected when the pixelwise classification algorithms are applied to high-resolution satellite images, in which the spatial relationship among the pixels is ignored. For classifying the multi-temporal high resolution images with limited labelled samples, spectral drift and "pepper and salt" problem, an object-based manifold alignment method is proposed. Firstly, multi-temporal multispectral images are cut to superpixels by simple linear iterative clustering (SLIC) respectively. Secondly, some features obtained from superpixels are formed as vector. Thirdly, a majority voting manifold alignment method aiming at solving high resolution problem is proposed and mapping the vector data to alignment space. At last, all the data in the alignment space are classified by using KNN method. Multi-temporal images from different areas or the same area are both considered in this paper. In the experiments, 2 groups of multi-temporal HR images collected by China GF1 and GF2 satellites are used for performance evaluation. Experimental results indicate that the proposed method not only has significantly outperforms than traditional domain adaptation methods in classification accuracy, but also effectively overcome the problem of "pepper and salt".

Remotely Piloted Aircraft Systems (RPAS) for high resolution topography and monitoring: civil protection purposes on hydrogeological contexts

NASA Astrophysics Data System (ADS)

Bertacchini, Eleonora; Castagnetti, Cristina; Corsini, Alessandro; De Cono, Stefano

2014-10-01

The proposed work concerns the analysis of Remotely Piloted Aircraft Systems (RPAS), also known as drones, UAV (Unmanned Aerial Vehicle) or UAS (Unmanned Aerial System), on hydrogeological contexts for civil protection purposes, underlying the advantages of using a flexible and relatively low cost system. The capabilities of photogrammetric RPAS multi-sensors platform were examined in term of mapping, creation of orthophotos, 3D models generation, data integration into a 3D GIS (Geographic Information System) and validation through independent techniques such as GNSS (Global Navigation Satellite System). The RPAS used (multirotor OktoXL, of the Mikrokopter) was equipped with a GPS (Global Positioning System) receiver, digital cameras for photos and videos, an inertial navigation system, a radio device for communication and telemetry, etc. This innovative way of viewing and understanding the environment showed huge potentialities for the study of the territory, and due to its characteristics could be well integrated with aircraft surveys. However, such characteristics seem to give priority to local applications for rigorous and accurate analysis, while it remains a means of expeditious investigation for more extended areas. According to civil protection purposes, the experimentation was carried out by simulating operational protocols, for example for inspection, surveillance, monitoring, land mapping, georeferencing methods (with or without Ground Control Points - GCP) based on high resolution topography (2D and 3D information).

Satellite Remote Sensing and Transportation Lifelines: Safety and Risk Analysis Along Rural Roads

NASA Astrophysics Data System (ADS)

Williamson, R.

the application of satellite Earth Observation (EO) methods to the analysis of transportation networks. Other geospatial technologies, including geographic information systems (GIS) and the Global Positioning System (GPS), sharply enhance the utility of EO data in identifying potential road hazards and providing an objective basis for allocating resources to reduce their risks. In combination, these powerful information technologies provide substantial public benefits and increased business opportunities to remote sensing value-added firms. departments in rural jurisdictions improve the trafficability of the roads under their management during severe weather. We are developing and testing these methods in the U.S. Southwest, where thousands of kilometers of unimproved and graded dirt roads cross Native American reservations. This generally arid region is nevertheless subject to periodic summer rainstorms and winter snow and ice, creating hazardous conditions for the region's transportation lifelines. Arizona and Southeast Utah, as well as digital terrain models from the U.S. Geological Survey. We have analyzed several risk factors, such as slope, road curvature, and intersections, by means of multi-criteria evaluation (MCE) on both unimproved and improved roads. In partnership with the Hopi Indian Nation in Arizona, we have acquired and analyzed GPS road centerline data and accident data that validate our methodology. hazards along paved and unpaved roads of the American Southwest. They are also transferable to the international settings, particularly in similarly arid climates.

Coarse-to-fine wavelet-based airport detection

NASA Astrophysics Data System (ADS)

Li, Cheng; Wang, Shuigen; Pang, Zhaofeng; Zhao, Baojun

2015-10-01

Airport detection on optical remote sensing images has attracted great interest in the applications of military optics scout and traffic control. However, most of the popular techniques for airport detection from optical remote sensing images have three weaknesses: 1) Due to the characteristics of optical images, the detection results are often affected by imaging conditions, like weather situation and imaging distortion; and 2) optical images contain comprehensive information of targets, so that it is difficult for extracting robust features (e.g., intensity and textural information) to represent airport area; 3) the high resolution results in large data volume, which makes real-time processing limited. Most of the previous works mainly focus on solving one of those problems, and thus, the previous methods cannot achieve the balance of performance and complexity. In this paper, we propose a novel coarse-to-fine airport detection framework to solve aforementioned three issues using wavelet coefficients. The framework includes two stages: 1) an efficient wavelet-based feature extraction is adopted for multi-scale textural feature representation, and support vector machine(SVM) is exploited for classifying and coarsely deciding airport candidate region; and then 2) refined line segment detection is used to obtain runway and landing field of airport. Finally, airport recognition is achieved by applying the fine runway positioning to the candidate regions. Experimental results show that the proposed approach outperforms the existing algorithms in terms of detection accuracy and processing efficiency.

Data quality control in eco-environmental monitoring

NASA Astrophysics Data System (ADS)

Lu, Chunyan; Wang, Jing

2007-11-01

With the development of science and technology, a number of environmental issues, such as sustainable development, climate change, environmental pollution, and land degradation become serious. Greater attention has been attached to environmental protection. The government gradually launched some eco--environmental construction projects. In 1999, China begin to carry out the project of Grain-for-Green in the west, to improve the eco-environment, and it make some good effect, but there are some questions that still can not be answered. How about the new grass or forest? Where are they? How can we do in the future? To answer these questions, the government began to monitor the eco-environment, based on remote sensing technology. Geography information can be attained timely, but the issue of uncertainty has become increasingly recognized, and this uncertainty affects the reliability of applications using the data. This article analyzed the process of eco-environment monitoring, the uncertainty of geography information, and discussed the methods of data quality control. The Spot5 span data and multi-spectral data in 2003(2002) were used, combined with land use survey data at the scale of 1:10,000, topography data at the scale of 1:10,000, and the local Grain-for-Green project map. Also the social and economic data were collected. Eco-environmental monitoring is a process which consists of several steps, such as image geometric correction, image matching, information extraction, and so on. Based on visual and automated method, land information turned to grass and forest from cultivated land was obtained by comparing the information form remote sensing data with the land survey data, and local Grain-for-Green project data, combined with field survey. According to the process, the uncertainty in the process was analyzed. Positional uncertainty, attribute uncertainty, and thematic uncertainty was obvious. Positional uncertainty mainly derived from image geometric correction, such as data resource, the number and spatial distribution of the control points are important resource of uncertainty. Attribution uncertainty mainly derived from the process of information extraction. Land classification system, artificial error was the main factor induced uncertainty. Concept defined was not clear, and it reduced thematic uncertainty. According to the resource of uncertainty, data quality control methods were put forward to improve the data quality. At first, it is more important to choose appropriate remote sensing data and other basic data. Secondly, the accuracy of digital orthophoto map should be controlled. Thirdly, it is necessary to check the result data according to relative data quality criterion to guarantee GIS data quality.

Cryptanalysis and Improvement of a Biometric-Based Multi-Server Authentication and Key Agreement Scheme.

PubMed

Wang, Chengqi; Zhang, Xiao; Zheng, Zhiming

2016-01-01

With the security requirements of networks, biometrics authenticated schemes which are applied in the multi-server environment come to be more crucial and widely deployed. In this paper, we propose a novel biometric-based multi-server authentication and key agreement scheme which is based on the cryptanalysis of Mishra et al.'s scheme. The informal and formal security analysis of our scheme are given, which demonstrate that our scheme satisfies the desirable security requirements. The presented scheme provides a variety of significant functionalities, in which some features are not considered in the most of existing authentication schemes, such as, user revocation or re-registration and biometric information protection. Compared with several related schemes, our scheme has more secure properties and lower computation cost. It is obviously more appropriate for practical applications in the remote distributed networks.

Pan-Tropical Forest Mapping by Exploiting Textures of Multi-Temporal High Resolution SAR Data

NASA Astrophysics Data System (ADS)

Knuth, R.; Eckardt, R.; Richter, N.; Schmullius, C.

2012-12-01

Even though the first commitment period of the Kyoto Protocol is in the offing, there is still a strong demand for profound, reliable, and up to date information in order to bridge the gap of knowledge of the land cover conversion. Despite the fact that land use change is one of the largest carbon contribution factors, it is still poorly quantified. This is particularly true for many tropical forest areas worldwide. Here, preservation of such pristine forest areas is critically endangered. Enormous population growth, the increasing global demand for various resources, and the ongoing unsustainable management practices put the remaining tropical forests under a huge pressure. Yet, only the United Nations Food and Agriculture Organization's (FAO) global Forest Resources Assessment (FRA) report provides the crucial quantitative information every 5 years on a regional scale. Nonetheless, the assembled information of the FRA reports bear the burden of ambiguity and vagueness, because they were compiled based on autonomously gathered statistics, which are usually driven by the individual country needs. There is a broad consensus among the different scientific disciplines, that only the remote sensing technology allows for a large scale robust monitoring of these widespread, and remote forest areas. Consequently, the FAO decided to supplementary analyze remote sensing data for the present (2010) and upcoming FRAs. However, it is also widely accepted that currently only microwave remote sensing techniques allow for an all-day, weather independent monitoring of the frequently cloud-covered tropics. In this context, high resolution Synthetic Aperture Radar (SAR) images of the German satellites TerraSAR-X and TanDEM-X have been investigated within the pan-tropics to support the latest FRA 2010 report. Data of more than 304 predominantly cloud-covered sites in Latin America (188), Central Africa (45) and Southeast Asia (71) have been acquired. Upon delivery, the corresponding radar images were processed using an operational processing chain that includes radiometric transformation, noise reduction, and georeferencing of the SAR data. In places with pronounced topography both satellites were used as single pass interferometer to derive a digital surface model in order to perform an orthorectification followed by a topographic normalization of the SAR backscatter values. As prescribed by the FAO, the final segment-based classification algorithm was fed by multi-temporal backscatter information, a set of textural features, and information on the degree of coherence between the multi-temporal acquisitions. Validation with available high resolution optical imagery suggests that the produced forest maps possess an overall accuracy of 75 percent or higher.

Non-destructive evaluation means and method of flaw reconstruction utilizing an ultrasonic multi-viewing transducer data acquistion system

DOEpatents

Thompson, Donald O.; Wormley, Samuel J.

1989-03-28

A multi-viewing ultrasound transducer acquisition system for non-destructive evaluation, flaw detection and flaw reconstruction in materials. A multiple transducer assembly includes a central transducer surrounded by a plurality of perimeter transducers, each perimeter transducer having an axis of transmission which can be angularly oriented with respect to the axis of transmission of the central transducer to intersect the axis of transmission of the central transducer. A control apparatus automatically and remotely positions the transducer assembly with respect to the material by a positioning apparatus and adjusts the pe GRANT REFERENCE This invention was conceived and reduced to practice at least in part under a grant from the Department of Energy under Contract No. W-7407-ENG-82.

Identification of Potential High-Risk Habitats within the Transmission Reach of Oncomelania hupensis after Floods Based on SAR Techniques in a Plane Region in China

PubMed Central

Shi, Yuanyuan; Qiu, Juan; Li, Rendong; Shen, Qiang; Huang, Duan

2017-01-01

Schistosomiasis japonica is an infectious disease caused by Schistosoma japonicum, and it remains endemic in China. Flooding is the main hazard factor, as it causes the spread of Oncomelania hupensis, the only intermediate host of Schistosoma japonicum, thereby triggering schistosomiasis outbreaks. Based on multi-source real-time remote sensing data, we used remote sensing (RS) technology, especially synthetic aperture radar (SAR), and geographic information system (GIS) techniques to carry out warning research on potential snail habitats within the snail dispersal range following flooding. Our research result demonstrated: (1) SAR data from Sentinel-1A before and during a flood were used to identify submerged areas rapidly and effectively; (2) the likelihood of snail survival was positively correlated with the clay proportion, core area standard deviation, and ditch length but negatively correlated with the wetness index, NDVI (normalized difference vegetation index), elevation, woodland area, and construction land area; (3) the snail habitats were most abundant near rivers and ditches in paddy fields; (4) the rivers and paddy irrigation ditches in the submerged areas must be the focused of mitigation efforts following future floods. PMID:28867814

Identification of Potential High-Risk Habitats within the Transmission Reach of Oncomelania hupensis after Floods Based on SAR Techniques in a Plane Region in China.

PubMed

Shi, Yuanyuan; Qiu, Juan; Li, Rendong; Shen, Qiang; Huang, Duan

2017-08-30

Schistosomiasis japonica is an infectious disease caused by Schistosoma japonicum , and it remains endemic in China. Flooding is the main hazard factor, as it causes the spread of Oncomelania hupensis , the only intermediate host of Schistosoma japonicum , thereby triggering schistosomiasis outbreaks. Based on multi-source real-time remote sensing data, we used remote sensing (RS) technology, especially synthetic aperture radar (SAR), and geographic information system (GIS) techniques to carry out warning research on potential snail habitats within the snail dispersal range following flooding. Our research result demonstrated: (1) SAR data from Sentinel-1A before and during a flood were used to identify submerged areas rapidly and effectively; (2) the likelihood of snail survival was positively correlated with the clay proportion, core area standard deviation, and ditch length but negatively correlated with the wetness index, NDVI (normalized difference vegetation index), elevation, woodland area, and construction land area; (3) the snail habitats were most abundant near rivers and ditches in paddy fields; (4) the rivers and paddy irrigation ditches in the submerged areas must be the focused of mitigation efforts following future floods.

Satellite remote sensing of harmful algal blooms: A new multi-algorithm method for detecting the Florida Red Tide (Karenia brevis).

PubMed

Carvalho, Gustavo A; Minnett, Peter J; Fleming, Lora E; Banzon, Viva F; Baringer, Warner

2010-06-01

In a continuing effort to develop suitable methods for the surveillance of Harmful Algal Blooms (HABs) of Karenia brevis using satellite radiometers, a new multi-algorithm method was developed to explore whether improvements in the remote sensing detection of the Florida Red Tide was possible. A Hybrid Scheme was introduced that sequentially applies the optimized versions of two pre-existing satellite-based algorithms: an Empirical Approach (using water-leaving radiance as a function of chlorophyll concentration) and a Bio-optical Technique (using particulate backscatter along with chlorophyll concentration). The long-term evaluation of the new multi-algorithm method was performed using a multi-year MODIS dataset (2002 to 2006; during the boreal Summer-Fall periods - July to December) along the Central West Florida Shelf between 25.75°N and 28.25°N. Algorithm validation was done with in situ measurements of the abundances of K. brevis; cell counts ≥1.5×10(4) cells l(-1) defined a detectable HAB. Encouraging statistical results were derived when either or both algorithms correctly flagged known samples. The majority of the valid match-ups were correctly identified (~80% of both HABs and non-blooming conditions) and few false negatives or false positives were produced (~20% of each). Additionally, most of the HAB-positive identifications in the satellite data were indeed HAB samples (positive predictive value: ~70%) and those classified as HAB-negative were almost all non-bloom cases (negative predictive value: ~86%). These results demonstrate an excellent detection capability, on average ~10% more accurate than the individual algorithms used separately. Thus, the new Hybrid Scheme could become a powerful tool for environmental monitoring of K. brevis blooms, with valuable consequences including leading to the more rapid and efficient use of ships to make in situ measurements of HABs.

Satellite remote sensing of harmful algal blooms: A new multi-algorithm method for detecting the Florida Red Tide (Karenia brevis)

PubMed Central

Carvalho, Gustavo A.; Minnett, Peter J.; Fleming, Lora E.; Banzon, Viva F.; Baringer, Warner

2010-01-01

In a continuing effort to develop suitable methods for the surveillance of Harmful Algal Blooms (HABs) of Karenia brevis using satellite radiometers, a new multi-algorithm method was developed to explore whether improvements in the remote sensing detection of the Florida Red Tide was possible. A Hybrid Scheme was introduced that sequentially applies the optimized versions of two pre-existing satellite-based algorithms: an Empirical Approach (using water-leaving radiance as a function of chlorophyll concentration) and a Bio-optical Technique (using particulate backscatter along with chlorophyll concentration). The long-term evaluation of the new multi-algorithm method was performed using a multi-year MODIS dataset (2002 to 2006; during the boreal Summer-Fall periods – July to December) along the Central West Florida Shelf between 25.75°N and 28.25°N. Algorithm validation was done with in situ measurements of the abundances of K. brevis; cell counts ≥1.5×104 cells l−1 defined a detectable HAB. Encouraging statistical results were derived when either or both algorithms correctly flagged known samples. The majority of the valid match-ups were correctly identified (~80% of both HABs and non-blooming conditions) and few false negatives or false positives were produced (~20% of each). Additionally, most of the HAB-positive identifications in the satellite data were indeed HAB samples (positive predictive value: ~70%) and those classified as HAB-negative were almost all non-bloom cases (negative predictive value: ~86%). These results demonstrate an excellent detection capability, on average ~10% more accurate than the individual algorithms used separately. Thus, the new Hybrid Scheme could become a powerful tool for environmental monitoring of K. brevis blooms, with valuable consequences including leading to the more rapid and efficient use of ships to make in situ measurements of HABs. PMID:21037979

A Web-GIS Procedure Based on Satellite Multi-Spectral and Airborne LIDAR Data to Map the Road blockage Due to seismic Damages of Built-Up Urban Areas

NASA Astrophysics Data System (ADS)

Costanzo, Antonio; Montuori, Antonio; Silva, Juan Pablo; Silvestri, Malvina; Musacchio, Massimo; Buongiorno, Maria Fabrizia; Stramondo, Salvatore

2016-08-01

In this work, a web-GIS procedure to map the risk of road blockage in urban environments through the combined use of space-borne and airborne remote sensing sensors is presented. The methodology concerns (1) the provision of a geo-database through the integration of space-borne multispectral images and airborne LiDAR data products; (2) the modeling of building vulnerability, based on the corresponding 3D geometry and construction time information; (3) the GIS-based mapping of road closure due to seismic- related building collapses based on the building characteristic height and the width of the road. Experimental results, gathered for the Cosenza urban area, allow demonstrating the benefits of both the proposed approach and the GIS-based integration of multi-platforms remote sensing sensors and techniques for seismic road assessment purposes.

Remote sensing and the Mississippi high accuracy reference network

NASA Technical Reports Server (NTRS)

Mick, Mark; Alexander, Timothy M.; Woolley, Stan

1994-01-01

Since 1986, NASA's Commercial Remote Sensing Program (CRSP) at Stennis Space Center has supported commercial remote sensing partnerships with industry. CRSP's mission is to maximize U.S. market exploitation of remote sensing and related space-based technologies and to develop advanced technical solutions for spatial information requirements. Observation, geolocation, and communications technologies are converging and their integration is critical to realize the economic potential for spatial informational needs. Global positioning system (GPS) technology enables a virtual revolution in geopositionally accurate remote sensing of the earth. A majority of states are creating GPS-based reference networks, or high accuracy reference networks (HARN). A HARN can be defined for a variety of local applications and tied to aerial or satellite observations to provide an important contribution to geographic information systems (GIS). This paper details CRSP's experience in the design and implementation of a HARN in Mississippi and the design and support of future applications of integrated earth observations, geolocation, and communications technology.

Multi-controller quantum teleportation with remote rotation and its applications

NASA Astrophysics Data System (ADS)

Kao, Shih-Hung; Chen, Yu-Ting; Tsai, Chia-Wei; Hwang, Tzonelih

2015-12-01

This work proposes the first multi-controller quantum teleportation with remote rotations, which allows a sender to teleport an arbitrary qubit to a receiver and at the same time, many controllers can remotely perform two kinds of rotation operations with various angles on the teleported qubit. In order to show its usefulness, a controlled quantum teleportation protocol has also been proposed.

A Weld Position Recognition Method Based on Directional and Structured Light Information Fusion in Multi-Layer/Multi-Pass Welding.

PubMed

Zeng, Jinle; Chang, Baohua; Du, Dong; Wang, Li; Chang, Shuhe; Peng, Guodong; Wang, Wenzhu

2018-01-05

Multi-layer/multi-pass welding (MLMPW) technology is widely used in the energy industry to join thick components. During automatic welding using robots or other actuators, it is very important to recognize the actual weld pass position using visual methods, which can then be used not only to perform reasonable path planning for actuators, but also to correct any deviations between the welding torch and the weld pass position in real time. However, due to the small geometrical differences between adjacent weld passes, existing weld position recognition technologies such as structured light methods are not suitable for weld position detection in MLMPW. This paper proposes a novel method for weld position detection, which fuses various kinds of information in MLMPW. First, a synchronous acquisition method is developed to obtain various kinds of visual information when directional light and structured light sources are on, respectively. Then, interferences are eliminated by fusing adjacent images. Finally, the information from directional and structured light images is fused to obtain the 3D positions of the weld passes. Experiment results show that each process can be done in 30 ms and the deviation is less than 0.6 mm. The proposed method can be used for automatic path planning and seam tracking in the robotic MLMPW process as well as electron beam freeform fabrication process.

Introduction to Remote Sensing Image Registration

NASA Technical Reports Server (NTRS)

Le Moigne, Jacqueline

2017-01-01

For many applications, accurate and fast image registration of large amounts of multi-source data is the first necessary step before subsequent processing and integration. Image registration is defined by several steps and each step can be approached by various methods which all present diverse advantages and drawbacks depending on the type of data, the type of applications, the a prior information known about the data and the type of accuracy that is required. This paper will first present a general overview of remote sensing image registration and then will go over a few specific methods and their applications

Serving Satellite Remote Sensing Data to User Community through the OGC Interoperability Protocols

NASA Astrophysics Data System (ADS)

di, L.; Yang, W.; Bai, Y.

2005-12-01

Remote sensing is one of the major methods for collecting geospatial data. Hugh amount of remote sensing data has been collected by space agencies and private companies around the world. For example, NASA's Earth Observing System (EOS) is generating more than 3 Tb of remote sensing data per day. The data collected by EOS are processed, distributed, archived, and managed by the EOS Data and Information System (EOSDIS). Currently, EOSDIS is managing several petabytes of data. All of those data are not only valuable for global change research, but also useful for local and regional application and decision makings. How to make the data easily accessible to and usable by the user community is one of key issues for realizing the full potential of these valuable datasets. In the past several years, the Open Geospatial Consortium (OGC) has developed several interoperability protocols aiming at making geospatial data easily accessible to and usable by the user community through Internet. The protocols particularly relevant to the discovery, access, and integration of multi-source satellite remote sensing data are the Catalog Service for Web (CS/W) and Web Coverage Services (WCS) Specifications. The OGC CS/W specifies the interfaces, HTTP protocol bindings, and a framework for defining application profiles required to publish and access digital catalogues of metadata for geographic data, services, and related resource information. The OGC WCS specification defines the interfaces between web-based clients and servers for accessing on-line multi-dimensional, multi-temporal geospatial coverage in an interoperable way. Based on definitions by OGC and ISO 19123, coverage data include all remote sensing images as well as gridded model outputs. The Laboratory for Advanced Information Technology and Standards (LAITS), George Mason University, has been working on developing and implementing OGC specifications for better serving NASA Earth science data to the user community for many years. We have developed the NWGISS software package that implements multiple OGC specifications, including OGC WMS, WCS, CS/W, and WFS. As a part of NASA REASON GeoBrain project, the NWGISS WCS and CS/W servers have been extended to provide operational access to NASA EOS data at data pools through OGC protocols and to make both services chainable in the web-service chaining. The extensions in the WCS server include the implementation of WCS 1.0.0 and WCS 1.0.2, and the development of WSDL description of the WCS services. In order to find the on-line EOS data resources, the CS/W server is extended at the backend to search metadata in NASA ECHO. This presentation reports those extensions and discuss lessons-learned on the implementation. It also discusses the advantage, disadvantages, and future improvement of OGC specifications, particularly the WCS.

Development of a generalized multi-pixel and multi-parameter satellite remote sensing algorithm for aerosol properties

NASA Astrophysics Data System (ADS)

Hashimoto, M.; Nakajima, T.; Takenaka, H.; Higurashi, A.

2013-12-01

We develop a new satellite remote sensing algorithm to retrieve the properties of aerosol particles in the atmosphere. In late years, high resolution and multi-wavelength, and multiple-angle observation data have been obtained by grand-based spectral radiometers and imaging sensors on board the satellite. With this development, optimized multi-parameter remote sensing methods based on the Bayesian theory have become popularly used (Turchin and Nozik, 1969; Rodgers, 2000; Dubovik et al., 2000). Additionally, a direct use of radiation transfer calculation has been employed for non-linear remote sensing problems taking place of look up table methods supported by the progress of computing technology (Dubovik et al., 2011; Yoshida et al., 2011). We are developing a flexible multi-pixel and multi-parameter remote sensing algorithm for aerosol optical properties. In this algorithm, the inversion method is a combination of the MAP method (Maximum a posteriori method, Rodgers, 2000) and the Phillips-Twomey method (Phillips, 1962; Twomey, 1963) as a smoothing constraint for the state vector. Furthermore, we include a radiation transfer calculation code, Rstar (Nakajima and Tanaka, 1986, 1988), numerically solved each time in iteration for solution search. The Rstar-code has been directly used in the AERONET operational processing system (Dubovik and King, 2000). Retrieved parameters in our algorithm are aerosol optical properties, such as aerosol optical thickness (AOT) of fine mode, sea salt, and dust particles, a volume soot fraction in fine mode particles, and ground surface albedo of each observed wavelength. We simultaneously retrieve all the parameters that characterize pixels in each of horizontal sub-domains consisting the target area. Then we successively apply the retrieval method to all the sub-domains in the target area. We conducted numerical tests for the retrieval of aerosol properties and ground surface albedo for GOSAT/CAI imager data to test the algorithm for the land area. In this test, we simulated satellite-observed radiances for a sub-domain consisting of 5 by 5 pixels by the Rstar code assuming wavelengths of 380, 674, 870 and 1600 [nm], atmospheric condition of the US standard atmosphere, and the several aerosol and ground surface conditions. The result of the experiment showed that AOTs of fine mode and dust particles, soot fraction and ground surface albedo at the wavelength of 674 [nm] are retrieved within absolute value differences of 0.04, 0.01, 0.06 and 0.006 from the true value, respectively, for the case of dark surface, and also, for the case of blight surface, 0.06, 0.03, 0.04 and 0.10 from the true value, respectively. We will conduct more tests to study the information contents of parameters needed for aerosol and land surface remote sensing with different boundary conditions among sub-domains.

Networked Guidance and Control for Mobile Multi-Agent Systems: A Multi-Terminal (Network) Information Theoretic Approach

DTIC Science & Technology

2014-11-04

maximization. A numerical example is provided to illustrate these ideas. [2] Marcos M. Vasconcelos and Nuno C. Martins, “Remote Estimation Games...and bias are affected by current and past outputs. (Working papers to be submitted until the end of 2014) [7] Marcos M. Vasconcelos and Nuno C...4.  Personnel:     Students  partially   funded  by  this  grant  E.  Arvelo,  S.  Park,  D.  Ward,  M.   Vasconcelos

Analysis of 2015 Winter In-Flight Icing Case Studies with Ground-Based Remote Sensing Systems Compared to In-Situ SLW Sondes

NASA Technical Reports Server (NTRS)

Serke, David J.; King, Michael Christopher; Hansen, Reid; Reehorst, Andrew L.

2016-01-01

National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) have developed an icing remote sensing technology that has demonstrated skill at detecting and classifying icing hazards in a vertical column above an instrumented ground station. This technology has recently been extended to provide volumetric coverage surrounding an airport. Building on the existing vertical pointing system, the new method for providing volumetric coverage utilizes a vertical pointing cloud radar, a multi-frequency microwave radiometer with azimuth and elevation pointing, and a NEXRAD radar. The new terminal area icing remote sensing system processes the data streams from these instruments to derive temperature, liquid water content, and cloud droplet size for each examined point in space. These data are then combined to ultimately provide icing hazard classification along defined approach paths into an airport. To date, statistical comparisons of the vertical profiling technology have been made to Pilot Reports and Icing Forecast Products. With the extension into relatively large area coverage and the output of microphysical properties in addition to icing severity, the use of these comparators is not appropriate and a more rigorous assessment is required. NASA conducted a field campaign during the early months of 2015 to develop a database to enable the assessment of the new terminal area icing remote sensing system and further refinement of terminal area icing weather information technologies in general. In addition to the ground-based remote sensors listed earlier, in-situ icing environment measurements by weather balloons were performed to produce a comprehensive comparison database. Balloon data gathered consisted of temperature, humidity, pressure, super-cooled liquid water content, and 3-D position with time. Comparison data plots of weather balloon and remote measurements, weather balloon flight paths, bulk comparisons of integrated liquid water content and icing cloud extent agreement, and terminal-area hazard displays are presented. Discussions of agreement quality and paths for future development are also included.

Urban structure analysis of mega city Mexico City using multisensoral remote sensing data

NASA Astrophysics Data System (ADS)

Taubenböck, H.; Esch, T.; Wurm, M.; Thiel, M.; Ullmann, T.; Roth, A.; Schmidt, M.; Mehl, H.; Dech, S.

2008-10-01

Mega city Mexico City is ranked the third largest urban agglomeration to date around the globe. The large extension as well as dynamic urban transformation and sprawl processes lead to a lack of up-to-date and area-wide data and information to measure, monitor, and understand the urban situation. This paper focuses on the capabilities of multisensoral remotely sensed data to provide a broad range of products derived from one scientific field - remote sensing - to support urban managing and planning. Therefore optical data sets from the Landsat and Quickbird sensors as well as radar data from the Shuttle Radar Topography Mission (SRTM) and the TerraSAR-X sensor are utilised. Using the multi-sensoral data sets the analysis are scale-dependent. On the one hand change detection on city level utilising the derived urban footprints enables to monitor and to assess spatiotemporal urban transformation, areal dimension of urban sprawl, its direction, and the built-up density distribution over time. On the other hand, structural characteristics of an urban landscape - the alignment and types of buildings, streets and open spaces - provide insight in the very detailed physical pattern of urban morphology on higher scale. The results show high accuracies of the derived multi-scale products. The multi-scale analysis allows quantifying urban processes and thus leading to an assessment and interpretation of urban trends.

Empirical validation and proof of added value of MUSICA's tropospheric δD remote sensing products

NASA Astrophysics Data System (ADS)

Schneider, M.; González, Y.; Dyroff, C.; Christner, E.; Wiegele, A.; Barthlott, S.; García, O. E.; Sepúlveda, E.; Hase, F.; Andrey, J.; Blumenstock, T.; Guirado, C.; Ramos, R.; Rodríguez, S.

2014-07-01

The project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) integrates tropospheric water vapour isototopologue remote sensing and in-situ observations. This paper presents a first empirical validation of MUSICA's H2O and δD remote sensing products (generated from ground-based FTIR, Fourier Transform InfraRed, spectrometer and space-based IASI, Infrared Atmospheric Sounding Interferometer, observation). As reference we use well calibrated in-situ measurements made aboard an aircraft (between 200 and 6800 m a.s.l.) by the dedicated ISOWAT instrument and on the island of Tenerife at two different altitudes (at Izaña, 2370 m a.s.l., and at Teide, 3550 m a.s.l.) by two commercial Picarro L2120-i water isotopologue analysers. The comparison to the ISOWAT profile measurements shows that the remote sensors can well capture the variations in the water vapour isotopologues and the scatter with respect to the in-situ references suggests a δD random uncertainty for the FTIR product of much better than 45‰ in the lower troposphere and of about 15‰ for the middle troposphere. For the middle tropospheric IASI δD product the study suggests a respective uncertainty of about 15‰. In addition, we find indications for a positive δD bias in the remote sensing products. The δD data are scientifically interesting only if they add information to the H2O observations. We are able to qualitatively demonstrate the added value of the MUSICA δD remote sensing data by comparing δD-vs.-H2O curves. First, we show that the added value of δD as seen in the Picarro data is similarly seen in FTIR data measured in coincidence. Second, we document that the δD-vs.-H2O curves obtained from the different in-situ and remote sensing data sets (ISOWAT, Picarro at Izaña and Teide, FTIR, and IASI) consistently identify two different moisture transport pathways to the subtropical north eastern Atlantic free troposphere.

The development of a tele-monitoring system for physiological parameters based on the B/S model.

PubMed

Shuicai, Wu; Peijie, Jiang; Chunlan, Yang; Haomin, Li; Yanping, Bai

2010-01-01

The development of a new physiological multi-parameter remote monitoring system is based on the B/S model. The system consists of a server monitoring center, Internet network and PC-based multi-parameter monitors. Using the B/S model, the clients can browse web pages via the server monitoring center and download and install ActiveX controls. The physiological multi-parameters are collected, displayed and remotely transmitted. The experimental results show that the system is stable, reliable and operates in real time. The system is suitable for use in physiological multi-parameter remote monitoring for family and community healthcare. Copyright © 2010 Elsevier Ltd. All rights reserved.

Improving precise positioning of surgical robotic instruments by a three-side-view presentation system on telesurgery.

PubMed

Hori, Kenta; Kuroda, Tomohiro; Oyama, Hiroshi; Ozaki, Yasuhiko; Nakamura, Takehiko; Takahashi, Takashi

2005-12-01

For faultless collaboration among the surgeon, surgical staffs, and surgical robots in telesurgery, communication must include environmental information of the remote operating room, such as behavior of robots and staffs, vital information of a patient, named supporting information, in addition to view of surgical field. "Surgical Cockpit System, " which is a telesurgery support system that has been developed by the authors, is mainly focused on supporting information exchange between remote sites. Live video presentation is important technology for Surgical Cockpit System. Visualization method to give precise location/posture of surgical instruments is indispensable for accurate control and faultless operation. In this paper, the authors propose three-side-view presentation method for precise location/posture control of surgical instruments in telesurgery. The experimental results show that the proposed method improved accurate positioning of a telemanipulator.

Spatial and radiometric characterization of multi-spectrum satellite images through multi-fractal analysis

NASA Astrophysics Data System (ADS)

Alonso, Carmelo; Tarquis, Ana M.; Zúñiga, Ignacio; Benito, Rosa M.

2017-03-01

Several studies have shown that vegetation indexes can be used to estimate root zone soil moisture. Earth surface images, obtained by high-resolution satellites, presently give a lot of information on these indexes, based on the data of several wavelengths. Because of the potential capacity for systematic observations at various scales, remote sensing technology extends the possible data archives from the present time to several decades back. Because of this advantage, enormous efforts have been made by researchers and application specialists to delineate vegetation indexes from local scale to global scale by applying remote sensing imagery. In this work, four band images have been considered, which are involved in these vegetation indexes, and were taken by satellites Ikonos-2 and Landsat-7 of the same geographic location, to study the effect of both spatial (pixel size) and radiometric (number of bits coding the image) resolution on these wavelength bands as well as two vegetation indexes: the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). In order to do so, a multi-fractal analysis of these multi-spectral images was applied in each of these bands and the two indexes derived. The results showed that spatial resolution has a similar scaling effect in the four bands, but radiometric resolution has a larger influence in blue and green bands than in red and near-infrared bands. The NDVI showed a higher sensitivity to the radiometric resolution than EVI. Both were equally affected by the spatial resolution. From both factors, the spatial resolution has a major impact in the multi-fractal spectrum for all the bands and the vegetation indexes. This information should be taken in to account when vegetation indexes based on different satellite sensors are obtained.

Enabling private and public sector organizations as agents of homeland security

NASA Astrophysics Data System (ADS)

Glassco, David H. J.; Glassco, Jordan C.

2006-05-01

Homeland security and defense applications seek to reduce the risk of undesirable eventualities across physical space in real-time. With that functional requirement in mind, our work focused on the development of IP based agent telecommunication solutions for heterogeneous sensor / robotic intelligent "Things" that could be deployed across the internet. This paper explains how multi-organization information and device sharing alliances may be formed to enable organizations to act as agents of homeland security (in addition to other uses). Topics include: (i) using location-aware, agent based, real-time information sharing systems to integrate business systems, mobile devices, sensor and actuator based devices and embedded devices used in physical infrastructure assets, equipment and other man-made "Things"; (ii) organization-centric real-time information sharing spaces using on-demand XML schema formatted networks; (iii) object-oriented XML serialization as a methodology for heterogeneous device glue code; (iv) how complex requirements for inter / intra organization information and device ownership and sharing, security and access control, mobility and remote communication service, tailored solution life cycle management, service QoS, service and geographic scalability and the projection of remote physical presence (through sensing and robotics) and remote informational presence (knowledge of what is going elsewhere) can be more easily supported through feature inheritance with a rapid agent system development methodology; (v) how remote object identification and tracking can be supported across large areas; (vi) how agent synergy may be leveraged with analytics to complement heterogeneous device networks.

Symbiotic Navigation in Multi-Robot Systems with Remote Obstacle Knowledge Sharing

PubMed Central

Ravankar, Abhijeet; Ravankar, Ankit A.; Kobayashi, Yukinori; Emaru, Takanori

2017-01-01

Large scale operational areas often require multiple service robots for coverage and task parallelism. In such scenarios, each robot keeps its individual map of the environment and serves specific areas of the map at different times. We propose a knowledge sharing mechanism for multiple robots in which one robot can inform other robots about the changes in map, like path blockage, or new static obstacles, encountered at specific areas of the map. This symbiotic information sharing allows the robots to update remote areas of the map without having to explicitly navigate those areas, and plan efficient paths. A node representation of paths is presented for seamless sharing of blocked path information. The transience of obstacles is modeled to track obstacles which might have been removed. A lazy information update scheme is presented in which only relevant information affecting the current task is updated for efficiency. The advantages of the proposed method for path planning are discussed against traditional method with experimental results in both simulation and real environments. PMID:28678193

Air Force JROTC: Introduction and Information Brief

DTIC Science & Technology

2015-04-01

Cadets, Prepared for the Future • Cadet Programs • Citizenship, leadership, academics, & extracurricular activities - our “Immersive Learning” tools...required Extracurricular Activities • Community Service Projects • Color Guard and Drill Teams • Marksmanship • Academic Bowl (SAT/ACT...optional & require principal approval Extracurricular Activities • New! Remote Controlled (RC) Multi-copters • Flight Simulators in Classrooms

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.

Palm Swamp Wetland Ecosystems of the Upper Amazon: Characterizing their Distribution and Inundation State Using Multiple Resolution Microwave Remote Sensing

NASA Astrophysics Data System (ADS)

Podest, E.; McDonald, K. C.; Schröder, R.; Pinto, N.; Zimmermann, R.; Horna, V.

2011-12-01

Palm swamp wetlands are prevalent in the Amazon basin, including extensive regions in northern Peru. These ecosystems are characterized by constant surface inundation and moderate seasonal water level variation. The combination of constantly saturated soils, giving rise to low oxygen conditions, and warm temperatures year-round can lead to considerable methane release to the atmosphere. Because of the widespread occurrence and expected sensitivity of these ecosystems to climate change, knowledge of their spatial extent and inundation state is crucial for assessing the associated land-atmosphere carbon exchange. Precise spatio-temporal information on palm swamps is difficult to gather because of their remoteness and difficult accessibility. Spaceborne microwave remote sensing is an effective tool for characterizing these ecosystems since it is sensitive to surface water and vegetation structure and allows monitoring large inaccessible areas on a temporal basis regardless of atmospheric conditions or solar illumination. We are developing a remote sensing methodology using multiple resolution microwave remote sensing data to determine palm swamp distribution and inundation state over focus regions in the Amazon basin in northern Peru. For this purpose, two types of multi-temporal microwave data are used: 1) high-resolution (100 m) data from the Advanced Land Observing Satellite (ALOS) Phased Array L-Band Synthetic Aperture Radar (PALSAR) to derive maps of palm swamp extent and inundation from dual-polarization fine-beam and multi-temporal HH-polarized ScanSAR, and 2) coarse resolution (25 km) combined active and passive microwave data from QuikSCAT and AMSR-E to derive inundated area fraction on a weekly basis. We compare information content and accuracy of the coarse resolution products to the PALSAR-based datasets to ensure information harmonization. The synergistic combination of high and low resolution datasets will allow for characterization of palm swamps and assessment of their flooding status. This work has been undertaken partly within the framework of the JAXA ALOS Kyoto & Carbon Initiative. PALSAR data have been provided by JAXA/EORC. Portions of this work were carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

Monitoring, analyzing and simulating of spatial-temporal changes of landscape pattern over mining area

NASA Astrophysics Data System (ADS)

Liu, Pei; Han, Ruimei; Wang, Shuangting

2014-11-01

According to the merits of remotely sensed data in depicting regional land cover and Land changes, multi- objective information processing is employed to remote sensing images to analyze and simulate land cover in mining areas. In this paper, multi-temporal remotely sensed data were selected to monitor the pattern, distri- bution and trend of LUCC and predict its impacts on ecological environment and human settlement in mining area. The monitor, analysis and simulation of LUCC in this coal mining areas are divided into five steps. The are information integration of optical and SAR data, LULC types extraction with SVM classifier, LULC trends simulation with CA Markov model, landscape temporal changes monitoring and analysis with confusion matrixes and landscape indices. The results demonstrate that the improved data fusion algorithm could make full use of information extracted from optical and SAR data; SVM classifier has an efficient and stable ability to obtain land cover maps, which could provide a good basis for both land cover change analysis and trend simulation; CA Markov model is able to predict LULC trends with good performance, and it is an effective way to integrate remotely sensed data with spatial-temporal model for analysis of land use / cover change and corresponding environmental impacts in mining area. Confusion matrixes are combined with landscape indices to evaluation and analysis show that, there was a sustained downward trend in agricultural land and bare land, but a continues growth trend tendency in water body, forest and other lands, and building area showing a wave like change, first increased and then decreased; mining landscape has undergone a from small to large and large to small process of fragmentation, agricultural land is the strongest influenced landscape type in this area, and human activities are the primary cause, so the problem should be pay more attentions by government and other organizations.

Regional Drought Monitoring Based on Multi-Sensor Remote Sensing

NASA Astrophysics Data System (ADS)

Rhee, Jinyoung; Im, Jungho; Park, Seonyoung

2014-05-01

Drought originates from the deficit of precipitation and impacts environment including agriculture and hydrological resources as it persists. The assessment and monitoring of drought has traditionally been performed using a variety of drought indices based on meteorological data, and recently the use of remote sensing data is gaining much attention due to its vast spatial coverage and cost-effectiveness. Drought information has been successfully derived from remotely sensed data related to some biophysical and meteorological variables and drought monitoring is advancing with the development of remote sensing-based indices such as the Vegetation Condition Index (VCI), Vegetation Health Index (VHI), and Normalized Difference Water Index (NDWI) to name a few. The Scaled Drought Condition Index (SDCI) has also been proposed to be used for humid regions proving the performance of multi-sensor data for agricultural drought monitoring. In this study, remote sensing-based hydro-meteorological variables related to drought including precipitation, temperature, evapotranspiration, and soil moisture were examined and the SDCI was improved by providing multiple blends of the multi-sensor indices for different types of drought. Multiple indices were examined together since the coupling and feedback between variables are intertwined and it is not appropriate to investigate only limited variables to monitor each type of drought. The purpose of this study is to verify the significance of each variable to monitor each type of drought and to examine the combination of multi-sensor indices for more accurate and timely drought monitoring. The weights for the blends of multiple indicators were obtained from the importance of variables calculated by non-linear optimization using a Machine Learning technique called Random Forest. The case study was performed in the Republic of Korea, which has four distinct seasons over the course of the year and contains complex topography with a variety of land cover types. Remote sensing data from the Tropical Rainfall Measuring Mission satellite (TRMM) and Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Microwave Scanning Radiometer-EOS (AMSR-E) sensors were obtained for the period from 2000 to 2012, and observation data from 99 weather stations, 441 streamflow gauges, as well as the gridded observation data from Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation of the Water Resources (APHRODITE) were obtained for validation. The objective blends of multiple indicators helped better assessment of various types of drought, and can be useful for drought early warning system. Since the improved SDCI is based on remotely sensed data, it can be easily applied to regions with limited or no observation data for drought assessment and monitoring.

A rapid extraction of landslide disaster information research based on GF-1 image

NASA Astrophysics Data System (ADS)

Wang, Sai; Xu, Suning; Peng, Ling; Wang, Zhiyi; Wang, Na

2015-08-01

In recent years, the landslide disasters occurred frequently because of the seismic activity. It brings great harm to people's life. It has caused high attention of the state and the extensive concern of society. In the field of geological disaster, landslide information extraction based on remote sensing has been controversial, but high resolution remote sensing image can improve the accuracy of information extraction effectively with its rich texture and geometry information. Therefore, it is feasible to extract the information of earthquake- triggered landslides with serious surface damage and large scale. Taking the Wenchuan county as the study area, this paper uses multi-scale segmentation method to extract the landslide image object through domestic GF-1 images and DEM data, which uses the estimation of scale parameter tool to determine the optimal segmentation scale; After analyzing the characteristics of landslide high-resolution image comprehensively and selecting spectrum feature, texture feature, geometric features and landform characteristics of the image, we can establish the extracting rules to extract landslide disaster information. The extraction results show that there are 20 landslide whose total area is 521279.31 .Compared with visual interpretation results, the extraction accuracy is 72.22%. This study indicates its efficient and feasible to extract earthquake landslide disaster information based on high resolution remote sensing and it provides important technical support for post-disaster emergency investigation and disaster assessment.

Cryptanalysis and Improvement of a Biometric-Based Multi-Server Authentication and Key Agreement Scheme

PubMed Central

Wang, Chengqi; Zhang, Xiao; Zheng, Zhiming

2016-01-01

With the security requirements of networks, biometrics authenticated schemes which are applied in the multi-server environment come to be more crucial and widely deployed. In this paper, we propose a novel biometric-based multi-server authentication and key agreement scheme which is based on the cryptanalysis of Mishra et al.’s scheme. The informal and formal security analysis of our scheme are given, which demonstrate that our scheme satisfies the desirable security requirements. The presented scheme provides a variety of significant functionalities, in which some features are not considered in the most of existing authentication schemes, such as, user revocation or re-registration and biometric information protection. Compared with several related schemes, our scheme has more secure properties and lower computation cost. It is obviously more appropriate for practical applications in the remote distributed networks. PMID:26866606

Radar and optical remote sensing in offshore domain to detect, characterize, and quantify ocean surface oil slicks

NASA Astrophysics Data System (ADS)

Angelliaume, S.; Ceamanos, X.; Viallefont-Robinet, F.; Baqué, R.; Déliot, Ph.; Miegebielle, V.

2017-10-01

Radar and optical sensors are operationally used by authorities or petroleum companies for detecting and characterizing maritime pollution. The interest lies not only in exploration but also in the monitoring of the maritime environment. Occurrence of natural seeps on the sea surface is a key indicator of the presence of mature source rock in the subsurface. These natural seeps, as well as the oil slicks, are commonly detected using radar sensors but the addition of optical imagery can deliver extra information such as the oil real fraction, which is critical for both exploration purposes and efficient cleanup operations. Today state-of-the-art approaches combine multiple data collected by optical and radar sensors embedded on-board different airborne and spaceborne platforms, to ensure wide spatial coverage and high frequency revisit time. Multi-wavelength imaging system may create a breakthrough in remote sensing applications, but it requires adapted processing techniques that need to be developed. To explore performances offered by multi-wavelength radar and optical sensors for oil slick monitoring, remote sensing data have been collected by SETHI, the airborne system developed by ONERA, during an oil spill cleanup exercise carried out in 2015 in the North Sea, Europe. The uniqueness of this data set lies in its high spatial resolution, low noise level and quasi-simultaneous acquisitions of different part of the electromagnetic spectrum. Specific processing techniques have been developed in order to extract meaningful information associated with oil-covered sea surface. Analysis of this unique and rich dataset demonstrates that remote sensing imagery, collected in both optical and microwave domains, allows to estimate slick surface properties such as the spatial abundance of oil and the relative concentration of hydrocarbons on the sea surface.

Multi-Data Approach for remote sensing-based regional crop rotation mapping: A case study for the Rur catchment, Germany

NASA Astrophysics Data System (ADS)

Waldhoff, Guido; Lussem, Ulrike; Bareth, Georg

2017-09-01

Spatial land use information is one of the key input parameters for regional agro-ecosystem modeling. Furthermore, to assess the crop-specific management in a spatio-temporal context accurately, parcel-related crop rotation information is additionally needed. Such data is scarcely available for a regional scale, so that only modeled crop rotations can be incorporated instead. However, the spectrum of the occurring multiannual land use patterns on arable land remains unknown. Thus, this contribution focuses on the mapping of the actually practiced crop rotations in the Rur catchment, located in the western part of Germany. We addressed this by combining multitemporal multispectral remote sensing data, ancillary information and expert-knowledge on crop phenology in a GIS-based Multi-Data Approach (MDA). At first, a methodology for the enhanced differentiation of the major crop types on an annual basis was developed. Key aspects are (i) the usage of physical block data to separate arable land from other land use types, (ii) the classification of remote sensing scenes of specific time periods, which are most favorable for the differentiation of certain crop types, and (iii) the combination of the multitemporal classification results in a sequential analysis strategy. Annual crop maps of eight consecutive years (2008-2015) were combined to a crop sequence dataset to have a profound data basis for the mapping of crop rotations. In most years, the remote sensing data basis was highly fragmented. Nevertheless, our method enabled satisfying crop mapping results. As an example for the annual crop mapping workflow, the procedure and the result of 2015 are illustrated. For the generation of the crop sequence dataset, the eight annual crop maps were geometrically smoothened and integrated into a single vector data layer. The resulting dataset informs about the occurring crop sequence for individual areas on arable land, so that crop rotation schemes can be derived. The resulting dataset reveals that the spectrum of the practiced crop rotations is extremely heterogeneous and contains a large amount of crop sequences, which strongly diverge from model crop rotations. Consequently, the integration of remote sensing-based crop rotation data can considerably reduce uncertainties regarding the management in regional agro-ecosystem modeling. Finally, the developed methods and the results are discussed in detail.

Mapping Palm Swamp Wetland Ecosystems in the Peruvian Amazon: a Multi-Sensor Remote Sensing Approach

NASA Astrophysics Data System (ADS)

Podest, E.; McDonald, K. C.; Schroeder, R.; Pinto, N.; Zimmerman, R.; Horna, V.

2012-12-01

Wetland ecosystems are prevalent in the Amazon basin, especially in northern Peru. Of specific interest are palm swamp wetlands because they are characterized by constant surface inundation and moderate seasonal water level variation. This combination of constantly saturated soils and warm temperatures year-round can lead to considerable methane release to the atmosphere. Because of the widespread occurrence and expected sensitivity of these ecosystems to climate change, it is critical to develop methods to quantify their spatial extent and inundation state in order to assess their carbon dynamics. Spatio-temporal information on palm swamps is difficult to gather because of their remoteness and difficult accessibility. Spaceborne microwave remote sensing is an effective tool for characterizing these ecosystems since it is sensitive to surface water and vegetation structure and allows monitoring large inaccessible areas on a temporal basis regardless of atmospheric conditions or solar illumination. We developed a remote sensing methodology using multi-sensor remote sensing data from the Advanced Land Observing Satellite (ALOS) Phased Array L-Band Synthetic Aperture Radar (PALSAR), Shuttle Radar Topography Mission (SRTM) DEM, and Landsat to derive maps at 100 meter resolution of palm swamp extent and inundation based on ground data collections; and combined active and passive microwave data from AMSR-E and QuikSCAT to derive inundation extent at 25 kilometer resolution on a weekly basis. We then compared information content and accuracy of the coarse resolution products relative to the high-resolution datasets. The synergistic combination of high and low resolution datasets allowed for characterization of palm swamps and assessment of their flooding status. This work has been undertaken partly within the framework of the JAXA ALOS Kyoto & Carbon Initiative. PALSAR data have been provided by JAXA. Portions of this work were carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

Vision-Based Haptic Feedback for Remote Micromanipulation in-SEM Environment

NASA Astrophysics Data System (ADS)

Bolopion, Aude; Dahmen, Christian; Stolle, Christian; Haliyo, Sinan; Régnier, Stéphane; Fatikow, Sergej

2012-07-01

This article presents an intuitive environment for remote micromanipulation composed of both haptic feedback and virtual reconstruction of the scene. To enable nonexpert users to perform complex teleoperated micromanipulation tasks, it is of utmost importance to provide them with information about the 3-D relative positions of the objects and the tools. Haptic feedback is an intuitive way to transmit such information. Since position sensors are not available at this scale, visual feedback is used to derive information about the scene. In this work, three different techniques are implemented, evaluated, and compared to derive the object positions from scanning electron microscope images. The modified correlation matching with generated template algorithm is accurate and provides reliable detection of objects. To track the tool, a marker-based approach is chosen since fast detection is required for stable haptic feedback. Information derived from these algorithms is used to propose an intuitive remote manipulation system that enables users situated in geographically distant sites to benefit from specific equipments, such as SEMs. Stability of the haptic feedback is ensured by the minimization of the delays, the computational efficiency of vision algorithms, and the proper tuning of the haptic coupling. Virtual guides are proposed to avoid any involuntary collisions between the tool and the objects. This approach is validated by a teleoperation involving melamine microspheres with a diameter of less than 2 μ m between Paris, France and Oldenburg, Germany.

Remote focusing for programmable multi-layer differential multiphoton microscopy

PubMed Central

Hoover, Erich E.; Young, Michael D.; Chandler, Eric V.; Luo, Anding; Field, Jeffrey J.; Sheetz, Kraig E.; Sylvester, Anne W.; Squier, Jeff A.

2010-01-01

We present the application of remote focusing to multiphoton laser scanning microscopy and utilize this technology to demonstrate simultaneous, programmable multi-layer imaging. Remote focusing is used to independently control the axial location of multiple focal planes that can be simultaneously imaged with single element detection. This facilitates volumetric multiphoton imaging in scattering specimens and can be practically scaled to a large number of focal planes. Further, it is demonstrated that the remote focusing control can be synchronized with the lateral scan directions, enabling imaging in orthogonal scan planes. PMID:21326641

A Webgis Framework for Disseminating Processed Remotely Sensed on Land Cover Transformations

NASA Astrophysics Data System (ADS)

Caradonna, Grazia; Novelli, Antonio; Tarantino, Eufemia; Cefalo, Raffaela; Fratino, Umberto

2016-06-01

Mediterranean regions have experienced significant soil degradation over the past decades. In this context, careful land observation using satellite data is crucial for understanding the long-term usage patterns of natural resources and facilitating their sustainable management to monitor and evaluate the potential degradation. Given the environmental and political interest on this problem, there is urgent need for a centralized repository and mechanism to share geospatial data, information and maps of land change. Geospatial data collecting is one of the most important task for many users because there are significant barriers in accessing and using data. This limit could be overcome by implementing a WebGIS through a combination of existing free and open source software for geographic information systems (FOSS4G). In this paper we preliminary discuss methods for collecting raster data in a geodatabase by processing open multi-temporal and multi-scale satellite data aimed at retrieving indicators for land degradation phenomenon (i.e. land cover/land use analysis, vegetation indices, trend analysis, etc.). Then we describe a methodology for designing a WebGIS framework in order to disseminate information through maps for territory monitoring. Basic WebGIS functions were extended with the help of POSTGIS database and OpenLayers libraries. Geoserver was customized to set up and enhance the website functions developing various advanced queries using PostgreSQL and innovative tools to carry out efficiently multi-layer overlay analysis. The end-product is a simple system that provides the opportunity not only to consult interactively but also download processed remote sensing data.

Research on visible and near infrared spectral-polarimetric properties of soil polluted by crude oil

NASA Astrophysics Data System (ADS)

Shen, Hui-yan; Zhou, Pu-cheng; Pan, Bang-long

2017-10-01

Hydrocarbon contaminated soil can impose detrimental effects on forest health and quality of agricultural products. To manage such consequences, oil leak indicators should be detected quickly by monitoring systems. Remote sensing is one of the most suitable techniques for monitoring systems, especially for areas which are uninhabitable and difficulty to access. The most available physical quantities in optical remote sensing domain are the intensity and spectral information obtained by visible or infrared sensors. However, besides the intensity and wavelength, polarization is another primary physical quantity associated with an optical field. During the course of reflecting light-wave, the surface of soil polluted by crude oil will cause polarimetric properties which are related to the nature of itself. Thus, detection of the spectralpolarimetric properties for soil polluted by crude oil has become a new remote sensing monitoring method. In this paper, the multi-angle spectral-polarimetric instrument was used to obtain multi-angle visible and near infrared spectralpolarimetric characteristic data of soil polluted by crude oil. And then, the change rule between polarimetric properties with different affecting factors, such as viewing zenith angle, incidence zenith angle of the light source, relative azimuth angle, waveband of the detector as well as different grain size of soil were discussed, so as to provide a scientific basis for the research on polarization remote sensing for soil polluted by crude oil.

A new multi-angle remote sensing framework for scaling vegetation properties from tower-based spectro-radiometers to next generation "CubeSat"-satellites.

NASA Astrophysics Data System (ADS)

Hilker, T.; Hall, F. G.; Dyrud, L. P.; Slagowski, S.

2014-12-01

Frequent earth observations are essential for assessing the risks involved with global climate change, its feedbacks on carbon, energy and water cycling and consequences for live on earth. Often, satellite-remote sensing is the only practical way to provide such observations at comprehensive spatial scales, but relationships between land surface parameters and remotely sensed observations are mostly empirical and cannot easily be scaled across larger areas or over longer time intervals. For instance, optically based methods frequently depend on extraneous effects that are unrelated to the surface property of interest, including the sun-server geometry or background reflectance. As an alternative to traditional, mono-angle techniques, multi-angle remote sensing can help overcome some of these limitations by allowing vegetation properties to be derived from comprehensive reflectance models that describe changes in surface parameters based on physical principles and radiative transfer theory. Recent results have shown in theoretical and experimental research that multi-angle techniques can be used to infer and scale the photosynthetic rate of vegetation, its biochemical and structural composition robustly from remote sensing. Multi-angle remote sensing could therefore revolutionize estimates of the terrestrial carbon uptake as scaling of primary productivity may provide a quantum leap in understanding the spatial and temporal complexity of terrestrial earth science. Here, we introduce a framework of next generation tower-based instruments to a novel and unique constellation of nano-satellites (Figure 1) that will allow us to systematically scale vegetation parameters from stand to global levels. We provide technical insights, scientific rationale and present results. We conclude that future earth observation from multi-angle satellite constellations, supported by tower based remote sensing will open new opportunities for earth system science and earth system modeling.

Analysis of fine-mode aerosol retrieval capabilities by different passive remote sensing instrument designs.

PubMed

Knobelspiesse, Kirk; Cairns, Brian; Mishchenko, Michael; Chowdhary, Jacek; Tsigaridis, Kostas; van Diedenhoven, Bastiaan; Martin, William; Ottaviani, Matteo; Alexandrov, Mikhail

2012-09-10

Remote sensing of aerosol optical properties is difficult, but multi-angle, multi-spectral, polarimetric instruments have the potential to retrieve sufficient information about aerosols that they can be used to improve global climate models. However, the complexity of these instruments means that it is difficult to intuitively understand the relationship between instrument design and retrieval success. We apply a Bayesian statistical technique that relates instrument characteristics to the information contained in an observation. Using realistic simulations of fine size mode dominated spherical aerosols, we investigate three instrument designs. Two of these represent instruments currently in orbit: the Multiangle Imaging SpectroRadiometer (MISR) and the POLarization and Directionality of the Earths Reflectances (POLDER). The third is the Aerosol Polarimetry Sensor (APS), which failed to reach orbit during recent launch, but represents a viable design for future instruments. The results show fundamental differences between the three, and offer suggestions for future instrument design and the optimal retrieval strategy for current instruments. Generally, our results agree with previous validation efforts of POLDER and airborne prototypes of APS, but show that the MISR aerosol optical thickness uncertainty characterization is possibly underestimated.

Benefits of Red-Edge Spectral Band and Texture Features for the Object-based Classification using RapidEye sSatellite Image data

NASA Astrophysics Data System (ADS)

Kim, H. O.; Yeom, J. M.

2014-12-01

Space-based remote sensing in agriculture is particularly relevant to issues such as global climate change, food security, and precision agriculture. Recent satellite missions have opened up new perspectives by offering high spatial resolution, various spectral properties, and fast revisit rates to the same regions. Here, we examine the utility of broadband red-edge spectral information in multispectral satellite image data for classifying paddy rice crops in South Korea. Additionally, we examine how object-based spectral features affect the classification of paddy rice growth stages. For the analysis, two seasons of RapidEye satellite image data were used. The results showed that the broadband red-edge information slightly improved the classification accuracy of the crop condition in heterogeneous paddy rice crop environments, particularly when single-season image data were used. This positive effect appeared to be offset by the multi-temporal image data. Additional texture information brought only a minor improvement or a slight decline, although it is well known to be advantageous for object-based classification in general. We conclude that broadband red-edge information derived from conventional multispectral satellite data has the potential to improve space-based crop monitoring. Because the positive or negative effects of texture features for object-based crop classification could barely be interpreted, the relationships between the textual properties and paddy rice crop parameters at the field scale should be further examined in depth.

Satellite image simulations for model-supervised, dynamic retrieval of crop type and land use intensity

NASA Astrophysics Data System (ADS)

Bach, H.; Klug, P.; Ruf, T.; Migdall, S.; Schlenz, F.; Hank, T.; Mauser, W.

2015-04-01

To support food security, information products about the actual cropping area per crop type, the current status of agricultural production and estimated yields, as well as the sustainability of the agricultural management are necessary. Based on this information, well-targeted land management decisions can be made. Remote sensing is in a unique position to contribute to this task as it is globally available and provides a plethora of information about current crop status. M4Land is a comprehensive system in which a crop growth model (PROMET) and a reflectance model (SLC) are coupled in order to provide these information products by analyzing multi-temporal satellite images. SLC uses modelled surface state parameters from PROMET, such as leaf area index or phenology of different crops to simulate spatially distributed surface reflectance spectra. This is the basis for generating artificial satellite images considering sensor specific configurations (spectral bands, solar and observation geometries). Ensembles of model runs are used to represent different crop types, fertilization status, soil colour and soil moisture. By multi-temporal comparisons of simulated and real satellite images, the land cover/crop type can be classified in a dynamically, model-supervised way and without in-situ training data. The method is demonstrated in an agricultural test-site in Bavaria. Its transferability is studied by analysing PROMET model results for the rest of Germany. Especially the simulated phenological development can be verified on this scale in order to understand whether PROMET is able to adequately simulate spatial, as well as temporal (intra- and inter-season) crop growth conditions, a prerequisite for the model-supervised approach. This sophisticated new technology allows monitoring of management decisions on the field-level using high resolution optical data (presently RapidEye and Landsat). The M4Land analysis system is designed to integrate multi-mission data and is well suited for the use of Sentinel-2's continuous and manifold data stream.

Neighborhood Landscape Spatial Patterns and Land Surface Temperature: An Empirical Study on Single-Family Residential Areas in Austin, Texas.

PubMed

Kim, Jun-Hyun; Gu, Donghwan; Sohn, Wonmin; Kil, Sung-Ho; Kim, Hwanyong; Lee, Dong-Kun

2016-09-02

Rapid urbanization has accelerated land use and land cover changes, and generated the urban heat island effect (UHI). Previous studies have reported positive effects of neighborhood landscapes on mitigating urban surface temperatures. However, the influence of neighborhood landscape spatial patterns on enhancing cooling effects has not yet been fully investigated. The main objective of this study was to assess the relationships between neighborhood landscape spatial patterns and land surface temperatures (LST) by using multi-regression models considering spatial autocorrelation issues. To measure the influence of neighborhood landscape spatial patterns on LST, this study analyzed neighborhood environments of 15,862 single-family houses in Austin, Texas, USA. Using aerial photos, geographic information systems (GIS), and remote sensing, FRAGSTATS was employed to calculate values of several landscape indices used to measure neighborhood landscape spatial patterns. After controlling for the spatial autocorrelation effect, results showed that larger and better-connected landscape spatial patterns were positively correlated with lower LST values in neighborhoods, while more fragmented and isolated neighborhood landscape patterns were negatively related to the reduction of LST.

Neighborhood Landscape Spatial Patterns and Land Surface Temperature: An Empirical Study on Single-Family Residential Areas in Austin, Texas

PubMed Central

Kim, Jun-Hyun; Gu, Donghwan; Sohn, Wonmin; Kil, Sung-Ho; Kim, Hwanyong; Lee, Dong-Kun

2016-01-01

Rapid urbanization has accelerated land use and land cover changes, and generated the urban heat island effect (UHI). Previous studies have reported positive effects of neighborhood landscapes on mitigating urban surface temperatures. However, the influence of neighborhood landscape spatial patterns on enhancing cooling effects has not yet been fully investigated. The main objective of this study was to assess the relationships between neighborhood landscape spatial patterns and land surface temperatures (LST) by using multi-regression models considering spatial autocorrelation issues. To measure the influence of neighborhood landscape spatial patterns on LST, this study analyzed neighborhood environments of 15,862 single-family houses in Austin, Texas, USA. Using aerial photos, geographic information systems (GIS), and remote sensing, FRAGSTATS was employed to calculate values of several landscape indices used to measure neighborhood landscape spatial patterns. After controlling for the spatial autocorrelation effect, results showed that larger and better-connected landscape spatial patterns were positively correlated with lower LST values in neighborhoods, while more fragmented and isolated neighborhood landscape patterns were negatively related to the reduction of LST. PMID:27598186

Optimal remote preparation of arbitrary multi-qubit real-parameter states via two-qubit entangled states

NASA Astrophysics Data System (ADS)

Wei, Jiahua; Shi, Lei; Luo, Junwen; Zhu, Yu; Kang, Qiaoyan; Yu, Longqiang; Wu, Hao; Jiang, Jun; Zhao, Boxin

2018-06-01

In this paper, we present an efficient scheme for remote state preparation of arbitrary n-qubit states with real coefficients. Quantum channel is composed of n maximally two-qubit entangled states, and several appropriate mutually orthogonal bases including the real parameters of prepared states are delicately constructed without the introduction of auxiliary particles. It is noted that the successful probability is 100% by using our proposal under the condition that the parameters of prepared states are all real. Compared to general states, the probability of our protocol is improved at the cost of the information reduction in the transmitted state.

Studies and Application of Remote Sensing Retrieval Method of Soil Moisture Content in Land Parcel Units in Irrigation Area

NASA Astrophysics Data System (ADS)

Zhu, H.; Zhao, H. L.; Jiang, Y. Z.; Zang, W. B.

2018-05-01

Soil moisture is one of the important hydrological elements. Obtaining soil moisture accurately and effectively is of great significance for water resource management in irrigation area. During the process of soil moisture content retrieval with multiremote sensing data, multi- remote sensing data always brings multi-spatial scale problems which results in inconformity of soil moisture content retrieved by remote sensing in different spatial scale. In addition, agricultural water use management has suitable spatial scale of soil moisture information so as to satisfy the demands of dynamic management of water use and water demand in certain unit. We have proposed to use land parcel unit as the minimum unit to do soil moisture content research in agricultural water using area, according to soil characteristics, vegetation coverage characteristics in underlying layer, and hydrological characteristic into the basis of study unit division. We have proposed division method of land parcel units. Based on multi thermal infrared and near infrared remote sensing data, we calculate the ndvi and tvdi index and make a statistical model between the tvdi index and soil moisture of ground monitoring station. Then we move forward to study soil moisture remote sensing retrieval method on land parcel unit scale. And the method has been applied in Hetao irrigation area. Results show that compared with pixel scale the soil moisture content in land parcel unit scale has displayed stronger correlation with true value. Hence, remote sensing retrieval method of soil moisture content in land parcel unit scale has shown good applicability in Hetao irrigation area. We converted the research unit into the scale of land parcel unit. Using the land parcel units with unified crops and soil attributes as the research units more complies with the characteristics of agricultural water areas, avoids the problems such as decomposition of mixed pixels and excessive dependence on high-resolution data caused by the research units of pixels, and doesn't involve compromises in the spatial scale and simulating precision like the grid simulation. When the application needs are met, the production efficiency of products can also be improved at a certain degree.

Integration of environmental simulation models with satellite remote sensing and geographic information systems technologies: case studies

USGS Publications Warehouse

Steyaert, Louis T.; Loveland, Thomas R.; Brown, Jesslyn F.; Reed, Bradley C.

1993-01-01

Environmental modelers are testing and evaluating a prototype land cover characteristics database for the conterminous United States developed by the EROS Data Center of the U.S. Geological Survey and the University of Nebraska Center for Advanced Land Management Information Technologies. This database was developed from multi temporal, 1-kilometer advanced very high resolution radiometer (AVHRR) data for 1990 and various ancillary data sets such as elevation, ecological regions, and selected climatic normals. Several case studies using this database were analyzed to illustrate the integration of satellite remote sensing and geographic information systems technologies with land-atmosphere interactions models at a variety of spatial and temporal scales. The case studies are representative of contemporary environmental simulation modeling at local to regional levels in global change research, land and water resource management, and environmental simulation modeling at local to regional levels in global change research, land and water resource management and environmental risk assessment. The case studies feature land surface parameterizations for atmospheric mesoscale and global climate models; biogenic-hydrocarbons emissions models; distributed parameter watershed and other hydrological models; and various ecological models such as ecosystem, dynamics, biogeochemical cycles, ecotone variability, and equilibrium vegetation models. The case studies demonstrate the important of multi temporal AVHRR data to develop to develop and maintain a flexible, near-realtime land cover characteristics database. Moreover, such a flexible database is needed to derive various vegetation classification schemes, to aggregate data for nested models, to develop remote sensing algorithms, and to provide data on dynamic landscape characteristics. The case studies illustrate how such a database supports research on spatial heterogeneity, land use, sensitivity analysis, and scaling issues involving regional extrapolations and parameterizations of dynamic land processes within simulation models.

Applying high resolution remote sensing image and DEM to falling boulder hazard assessment

NASA Astrophysics Data System (ADS)

Huang, Changqing; Shi, Wenzhong; Ng, K. C.

2005-10-01

Boulder fall hazard assessing generally requires gaining the boulder information. The extensive mapping and surveying fieldwork is a time-consuming, laborious and dangerous conventional method. So this paper proposes an applying image processing technology to extract boulder and assess boulder fall hazard from high resolution remote sensing image. The method can replace the conventional method and extract the boulder information in high accuracy, include boulder size, shape, height and the slope and aspect of its position. With above boulder information, it can be satisfied for assessing, prevention and cure boulder fall hazard.

Multi-modal cockpit interface for improved airport surface operations

NASA Technical Reports Server (NTRS)

Arthur, Jarvis J. (Inventor); Bailey, Randall E. (Inventor); Prinzel, III, Lawrence J. (Inventor); Kramer, Lynda J. (Inventor); Williams, Steven P. (Inventor)

2010-01-01

A system for multi-modal cockpit interface during surface operation of an aircraft comprises a head tracking device, a processing element, and a full-color head worn display. The processing element is configured to receive head position information from the head tracking device, to receive current location information of the aircraft, and to render a virtual airport scene corresponding to the head position information and the current aircraft location. The full-color head worn display is configured to receive the virtual airport scene from the processing element and to display the virtual airport scene. The current location information may be received from one of a global positioning system or an inertial navigation system.

Eco-hydrological Wireless Sensor Network and upscaling method research in the Heihe River Basin, China

NASA Astrophysics Data System (ADS)

Jin, Rui; kang, Jian

2017-04-01

Wireless Sensor Networks are recognized as one of most important near-surface components of GEOSS (Global Earth Observation System of Systems), with flourish development of low-cost, robust and integrated data loggers and sensors. A nested eco-hydrological wireless sensor network (EHWSN) was installed in the up- and middle-reaches of the Heihe River Basin, operated to obtain multi-scale observation of soil moisture, soil temperature and land surface temperature from 2012 till now. The spatial distribution of EHWSN was optimally designed based on the geo-statistical theory, with the aim to capture the spatial variations and temporal dynamics of soil moisture and soil temperature, and to produce ground truth at grid scale for validating the related remote sensing products and model simulation in the heterogeneous land surface. In terms of upscaling research, we have developed a set of method to aggregate multi-point WSN observations to grid scale ( 1km), including regression kriging estimation to utilize multi-resource remote sensing auxiliary information, block kriging with homogeneous measurement errors, and bayesian-based upscaling algorithm that utilizes MODIS-derived apparent thermal inertia. All the EHWSN observation are organized as datasets to be freely published at http://westdc.westgis.ac.cn/hiwater. EHWSN integrates distributed observation nodes to achieve an automated, intelligent and remote-controllable network that provides superior integrated, standardized and automated observation capabilities for hydrological and ecological processes research at the basin scale.

An evaluation of a UAV guidance system with consumer grade GPS receivers

NASA Astrophysics Data System (ADS)

Rosenberg, Abigail Stella

Remote sensing has been demonstrated an important tool in agricultural and natural resource management and research applications, however there are limitations that exist with traditional platforms (i.e., hand held sensors, linear moves, vehicle mounted, airplanes, remotely piloted vehicles (RPVs), unmanned aerial vehicles (UAVs) and satellites). Rapid technological advances in electronics, computers, software applications, and the aerospace industry have dramatically reduced the cost and increased the availability of remote sensing technologies. Remote sensing imagery vary in spectral, spatial, and temporal resolutions and are available from numerous providers. Appendix A presented results of a test project that acquired high-resolution aerial photography with a RPV to map the boundary of a 0.42 km2 fire area. The project mapped the boundaries of the fire area from a mosaic of the aerial images collected and compared this with ground-based measurements. The project achieved a 92.4% correlation between the aerial assessment and the ground truth data. Appendix B used multi-objective analysis to quantitatively assess the tradeoffs between different sensor platform attributes to identify the best overall technology. Experts were surveyed to identify the best overall technology at three different pixel sizes. Appendix C evaluated the positional accuracy of a relatively low cost UAV designed for high resolution remote sensing of small areas in order to determine the positional accuracy of sensor readings. The study evaluated the accuracy and uncertainty of a UAV flight route with respect to the programmed waypoints and of the UAV's GPS position, respectively. In addition, the potential displacement of sensor data was evaluated based on (1) GPS measurements on board the aircraft and (2) the autopilot's circuit board with 3-axis gyros and accelerometers (i.e., roll, pitch, and yaw). The accuracies were estimated based on a 95% confidence interval or similar methods. The accuracy achieved in the second and third manuscripts demonstrates that reasonably priced, high resolution remote sensing via RPVs and UAVs is practical for agriculture and natural resource professionals.

Mobile-Cloud Assisted Video Summarization Framework for Efficient Management of Remote Sensing Data Generated by Wireless Capsule Sensors

PubMed Central

Mehmood, Irfan; Sajjad, Muhammad; Baik, Sung Wook

2014-01-01

Wireless capsule endoscopy (WCE) has great advantages over traditional endoscopy because it is portable and easy to use, especially in remote monitoring health-services. However, during the WCE process, the large amount of captured video data demands a significant deal of computation to analyze and retrieve informative video frames. In order to facilitate efficient WCE data collection and browsing task, we present a resource- and bandwidth-aware WCE video summarization framework that extracts the representative keyframes of the WCE video contents by removing redundant and non-informative frames. For redundancy elimination, we use Jeffrey-divergence between color histograms and inter-frame Boolean series-based correlation of color channels. To remove non-informative frames, multi-fractal texture features are extracted to assist the classification using an ensemble-based classifier. Owing to the limited WCE resources, it is impossible for the WCE system to perform computationally intensive video summarization tasks. To resolve computational challenges, mobile-cloud architecture is incorporated, which provides resizable computing capacities by adaptively offloading video summarization tasks between the client and the cloud server. The qualitative and quantitative results are encouraging and show that the proposed framework saves information transmission cost and bandwidth, as well as the valuable time of data analysts in browsing remote sensing data. PMID:25225874

Airborne and Ground-Based Optical Characterization of Legacy Underground Nuclear Test Sites

NASA Astrophysics Data System (ADS)

Vigil, S.; Craven, J.; Anderson, D.; Dzur, R.; Schultz-Fellenz, E. S.; Sussman, A. J.

2015-12-01

Detecting, locating, and characterizing suspected underground nuclear test sites is a U.S. security priority. Currently, global underground nuclear explosion monitoring relies on seismic and infrasound sensor networks to provide rapid initial detection of potential underground nuclear tests. While seismic and infrasound might be able to generally locate potential underground nuclear tests, additional sensing methods might be required to further pinpoint test site locations. Optical remote sensing is a robust approach for site location and characterization due to the ability it provides to search large areas relatively quickly, resolve surface features in fine detail, and perform these tasks non-intrusively. Optical remote sensing provides both cultural and surface geological information about a site, for example, operational infrastructure, surface fractures. Surface geological information, when combined with known or estimated subsurface geologic information, could provide clues concerning test parameters. We have characterized two legacy nuclear test sites on the Nevada National Security Site (NNSS), U20ak and U20az using helicopter-, ground- and unmanned aerial system-based RGB imagery and light detection and ranging (lidar) systems. The multi-faceted information garnered from these different sensing modalities has allowed us to build a knowledge base of how a nuclear test site might look when sensed remotely, and the standoff distances required to resolve important site characteristics.

Mobile-cloud assisted video summarization framework for efficient management of remote sensing data generated by wireless capsule sensors.

PubMed

Mehmood, Irfan; Sajjad, Muhammad; Baik, Sung Wook

2014-09-15

Wireless capsule endoscopy (WCE) has great advantages over traditional endoscopy because it is portable and easy to use, especially in remote monitoring health-services. However, during the WCE process, the large amount of captured video data demands a significant deal of computation to analyze and retrieve informative video frames. In order to facilitate efficient WCE data collection and browsing task, we present a resource- and bandwidth-aware WCE video summarization framework that extracts the representative keyframes of the WCE video contents by removing redundant and non-informative frames. For redundancy elimination, we use Jeffrey-divergence between color histograms and inter-frame Boolean series-based correlation of color channels. To remove non-informative frames, multi-fractal texture features are extracted to assist the classification using an ensemble-based classifier. Owing to the limited WCE resources, it is impossible for the WCE system to perform computationally intensive video summarization tasks. To resolve computational challenges, mobile-cloud architecture is incorporated, which provides resizable computing capacities by adaptively offloading video summarization tasks between the client and the cloud server. The qualitative and quantitative results are encouraging and show that the proposed framework saves information transmission cost and bandwidth, as well as the valuable time of data analysts in browsing remote sensing data.

Soft-information flipping approach in multi-head multi-track BPMR systems

NASA Astrophysics Data System (ADS)

Warisarn, C.; Busyatras, W.; Myint, L. M. M.

2018-05-01

Inter-track interference is one of the most severe impairments in bit-patterned media recording system. This impairment can be effectively handled by a modulation code and a multi-head array jointly processing multiple tracks; however, such a modulation constraint has never been utilized to improve the soft-information. Therefore, this paper proposes the utilization of modulation codes with an encoded constraint defined by the criteria for soft-information flipping during a three-track data detection process. Moreover, we also investigate the optimal offset position of readheads to provide the most improvement in system performance. The simulation results indicate that the proposed systems with and without position jitter are significantly superior to uncoded systems.

An improved feature extraction algorithm based on KAZE for multi-spectral image

NASA Astrophysics Data System (ADS)

Yang, Jianping; Li, Jun

2018-02-01

Multi-spectral image contains abundant spectral information, which is widely used in all fields like resource exploration, meteorological observation and modern military. Image preprocessing, such as image feature extraction and matching, is indispensable while dealing with multi-spectral remote sensing image. Although the feature matching algorithm based on linear scale such as SIFT and SURF performs strong on robustness, the local accuracy cannot be guaranteed. Therefore, this paper proposes an improved KAZE algorithm, which is based on nonlinear scale, to raise the number of feature and to enhance the matching rate by using the adjusted-cosine vector. The experiment result shows that the number of feature and the matching rate of the improved KAZE are remarkably than the original KAZE algorithm.

Assessing Wetland Hydroperiod and Soil Moisture With Remote Sensing: A Demonstration for the NASA Plum Brook Station Year 2

NASA Technical Reports Server (NTRS)

Brooks, Colin; Bourgeau-Chavez, Laura; Endres, Sarah; Battaglia, Michael; Shuchman, Robert

2015-01-01

Primary Goal: Assist with the evaluation and measuring of wetlands hydroperiod at the PlumBrook Station using multi-source remote sensing data as part of a larger effort on projecting climate change-related impacts on the station's wetland ecosystems. MTRI expanded on the multi-source remote sensing capabilities to help estimate and measure hydroperiod and the relative soil moisture of wetlands at NASA's Plum Brook Station. Multi-source remote sensing capabilities are useful in estimating and measuring hydroperiod and relative soil moisture of wetlands. This is important as a changing regional climate has several potential risks for wetland ecosystem function. The year two analysis built on the first year of the project by acquiring and analyzing remote sensing data for additional dates and types of imagery, combined with focused field work. Five deliverables were planned and completed: 1) Show the relative length of hydroperiod using available remote sensing datasets 2) Date linked table of wetlands extent over time for all feasible non-forested wetlands 3) Utilize LIDAR data to measure topographic height above sea level of all wetlands, wetland to catchment area radio, slope of wetlands, and other useful variables 4) A demonstration of how analyzed results from multiple remote sensing data sources can help with wetlands vulnerability assessment 5) A MTRI style report summarizing year 2 results. This report serves as a descriptive summary of our completion of these our deliverables. Additionally, two formal meetings were held with Larry Liou and Amanda Sprinzl to provide project updates and receive direction on outputs. These were held on 2/26/15 and 9/17/15 at the Plum Brook Station. Principal Component Analysis (PCA) is a multivariate statistical technique used to identify dominant spatial and temporal backscatter signatures. PCA reduces the information contained in the temporal dataset to the first few new Principal Component (PC) images. Some advantages of PCA include the ability to filter out temporal autocorrelation and reduce speckle to the higher order PC images. A PCA was performed using ERDAS Imagine on a time series of PALSAR dates. Hydroperiod maps were created by separating the PALSAR dates into two date ranges, 2006-2008 and 2010, and performing an unsupervised classification on the PCAs.

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

PubMed

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

2018-04-26

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.

Canadian SAR remote sensing for the Terrestrial Wetland Global Change Research Network (TWGCRN)

USGS Publications Warehouse

Kaya, Shannon; Brisco, Brian; Cull, Andrew; Gallant, Alisa L.; Sadinski, Walter J.; Thompson, Dean

2010-01-01

The Canada Centre for Remote Sensing (CCRS) has more than 30 years of experience investigating the use of SAR remote sensing for many applications related to terrestrial water resources. Recently, CCRS scientists began contributing to the Terrestrial Wetland Global Change Research Network (TWGCRN), a bi-national research network dedicated to assessing impacts of global change on interconnected wetland-upland landscapes across a vital portion of North America. CCRS scientists are applying SAR remote sensing to characterize wetland components of these landscapes in three ways. First, they are using a comprehensive set of RADARSAT-2 SAR data collected during April to September 2009 to extract multi-temporal surface water information for key TWGCRN study landscapes in North America. Second, they are analyzing polarimetric RADARSAT-2 data to determine areas where double-bounce represents the primary scattering mechanism and is indicative of flooded vegetation in these landscapes. Third, they are testing advanced interferometric SAR techniques to estimate water levels with RADARSAT-2 Fine Quad polarimetric image pairs. The combined information from these three SAR analysis activities will provide TWGCRN scientists with an integrated view and monitoring capability for these dynamic wetland-upland landscapes. These data are being used in conjunction with other remote sensing and field data to study interactions between landscape and animal (birds and amphibians) responses to climate/global change.

Combined Infrared Stereo and Laser Ranging Cloud Measurements from Shuttle Mission STS-85

NASA Technical Reports Server (NTRS)

Lancaster, Redgie S.; Spinhirne, James D.; OCStarr, David (Technical Monitor)

2001-01-01

Multi-angle remote sensing provides a wealth of information for earth and climate monitoring. And, as technology advances so do the options for developing instrumentation versatile enough to meet the demands associated with these types of measurements. In the current work, the multiangle measurement capability of the Infrared Spectral Imaging Radiometer is demonstrated. This instrument flew as part of mission STS-85 of the space shuttle Columbia in 1997 and was the first earth-observing radiometer to incorporate an uncooled microbolometer array detector as its image sensor. Specifically, a method for computing cloud-top height from the multi-spectral stereo measurements acquired during this flight has been developed and the results demonstrate that a vertical precision of 10.6 km was achieved. Further, the accuracy of these measurements is confirmed by comparison with coincident direct laser ranging measurements from the Shuttle Laser Altimeter. Mission STS-85 was the first space flight to combine laser ranging and thermal IR camera systems for cloud remote sensing.

A new method of Quickbird own image fusion

NASA Astrophysics Data System (ADS)

Han, Ying; Jiang, Hong; Zhang, Xiuying

2009-10-01

With the rapid development of remote sensing technology, the means of accessing to remote sensing data become increasingly abundant, thus the same area can form a large number of multi-temporal, different resolution image sequence. At present, the fusion methods are mainly: HPF, IHS transform method, PCA method, Brovey, Mallat algorithm and wavelet transform and so on. There exists a serious distortion of the spectrums in the IHS transform, Mallat algorithm omits low-frequency information of the high spatial resolution images, the integration results of which has obvious blocking effects. Wavelet multi-scale decomposition for different sizes, the directions, details and the edges can have achieved very good results, but different fusion rules and algorithms can achieve different effects. This article takes the Quickbird own image fusion as an example, basing on wavelet transform and HVS, wavelet transform and IHS integration. The result shows that the former better. This paper introduces the correlation coefficient, the relative average spectral error index and usual index to evaluate the quality of image.

Change Detection of Remote Sensing Images by Dt-Cwt and Mrf

NASA Astrophysics Data System (ADS)

Ouyang, S.; Fan, K.; Wang, H.; Wang, Z.

2017-05-01

Aiming at the significant loss of high frequency information during reducing noise and the pixel independence in change detection of multi-scale remote sensing image, an unsupervised algorithm is proposed based on the combination between Dual-tree Complex Wavelet Transform (DT-CWT) and Markov random Field (MRF) model. This method first performs multi-scale decomposition for the difference image by the DT-CWT and extracts the change characteristics in high-frequency regions by using a MRF-based segmentation algorithm. Then our method estimates the final maximum a posterior (MAP) according to the segmentation algorithm of iterative condition model (ICM) based on fuzzy c-means(FCM) after reconstructing the high-frequency and low-frequency sub-bands of each layer respectively. Finally, the method fuses the above segmentation results of each layer by using the fusion rule proposed to obtain the mask of the final change detection result. The results of experiment prove that the method proposed is of a higher precision and of predominant robustness properties.

[The progress in retrieving land surface temperature based on thermal infrared and microwave remote sensing technologies].

PubMed

Zhang, Jia-Hua; Li, Xin; Yao, Feng-Mei; Li, Xian-Hua

2009-08-01

Land surface temperature (LST) is an important parameter in the study on the exchange of substance and energy between land surface and air for the land surface physics process at regional and global scales. Many applications of satellites remotely sensed data must provide exact and quantificational LST, such as drought, high temperature, forest fire, earthquake, hydrology and the vegetation monitor, and the models of global circulation and regional climate also need LST as input parameter. Therefore, the retrieval of LST using remote sensing technology becomes one of the key tasks in quantificational remote sensing study. Normally, in the spectrum bands, the thermal infrared (TIR, 3-15 microm) and microwave bands (1 mm-1 m) are important for retrieval of the LST. In the present paper, firstly, several methods for estimating the LST on the basis of thermal infrared (TIR) remote sensing were synthetically reviewed, i. e., the LST measured with an ground-base infrared thermometer, the LST retrieval from mono-window algorithm (MWA), single-channel algorithm (SCA), split-window techniques (SWT) and multi-channels algorithm(MCA), single-channel & multi-angle algorithm and multi-channels algorithm & multi-angle algorithm, and retrieval method of land surface component temperature using thermal infrared remotely sensed satellite observation. Secondly, the study status of land surface emissivity (epsilon) was presented. Thirdly, in order to retrieve LST for all weather conditions, microwave remotely sensed data, instead of thermal infrared data, have been developed recently, and the LST retrieval method from passive microwave remotely sensed data was also introduced. Finally, the main merits and shortcomings of different kinds of LST retrieval methods were discussed, respectively.

Hyperspectral Remote Sensing of Foliar Nitrogen Content

NASA Technical Reports Server (NTRS)

Knyazikhin, Yuri; Schull, Mitchell A.; Stenberg, Pauline; Moettus, Matti; Rautiainen, Miina; Yang, Yan; Marshak, Alexander; Carmona, Pedro Latorre; Kaufmann, Robert K.; Lewis, Philip;

GIS prospectivity mapping and 3D modeling validation for potential uranium deposit targets in Shangnan district, China

NASA Astrophysics Data System (ADS)

Xie, Jiayu; Wang, Gongwen; Sha, Yazhou; Liu, Jiajun; Wen, Botao; Nie, Ming; Zhang, Shuai

2017-04-01

Integrating multi-source geoscience information (such as geology, geophysics, geochemistry, and remote sensing) using GIS mapping is one of the key topics and frontiers in quantitative geosciences for mineral exploration. GIS prospective mapping and three-dimensional (3D) modeling can be used not only to extract exploration criteria and delineate metallogenetic targets but also to provide important information for the quantitative assessment of mineral resources. This paper uses the Shangnan district of Shaanxi province (China) as a case study area. GIS mapping and potential granite-hydrothermal uranium targeting were conducted in the study area combining weights of evidence (WofE) and concentration-area (C-A) fractal methods with multi-source geoscience information. 3D deposit-scale modeling using GOCAD software was performed to validate the shapes and features of the potential targets at the subsurface. The research results show that: (1) the known deposits have potential zones at depth, and the 3D geological models can delineate surface or subsurface ore-forming features, which can be used to analyze the uncertainty of the shape and feature of prospectivity mapping at the subsurface; (2) single geochemistry anomalies or remote sensing anomalies at the surface require combining the depth exploration criteria of geophysics to identify potential targets; and (3) the single or sparse exploration criteria zone with few mineralization spots at the surface has high uncertainty in terms of the exploration target.

A systematic review of social support interventions for caregivers of people with dementia: Are they doing what they promise?

PubMed

Dam, Alieske E H; de Vugt, Marjolein E; Klinkenberg, Inge P M; Verhey, Frans R J; van Boxtel, Martin P J

2016-03-01

Social support interventions for caregivers of persons with dementia (PwD) are important because informal carers often rely on their social networks for support. This systematic review synthesises findings from research on social support interventions, and examines their methodological quality and effectiveness on caregiver social support and well-being variables. A systematic literature search utilised five databases. Papers were selected when the primary aim of the intervention was to improve social support. Quality of papers was assessed by the Level of Evidence grade and the criteria list from the Cochrane Back Review Group. 39 papers were identified and classified into 4 social support intervention categories: befriending and peer support, family support and social network interventions, support groups, and remote interventions using the internet or telephone. Content, intensity, uptake, effectiveness and quality of interventions varied widely. In general, the level of evidence was low. Most studies measured effect on well-being variables, while few examined social support outcomes. Multi-component social support interventions were most effective. Evidence suggested, also a caregiver benefit from remote interventions. Generally, results were inconsistent; some papers demonstrated beneficial results, while others demonstrated no improvement on social support and well-being variables. Social support outcomes were more positively evaluated when qualitative outcome measures rather than quantitative measures were used. Although multi-component social support interventions may improve caregiver well-being, there is insufficient evidence to conclude whether a change in social support is the underlying mediating factor. The inclusion, validation and operationalization of caregiver social support measures deserve more attention. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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.

Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks

NASA Astrophysics Data System (ADS)

Audebert, Nicolas; Le Saux, Bertrand; Lefèvre, Sébastien

2018-06-01

In this work, we investigate various methods to deal with semantic labeling of very high resolution multi-modal remote sensing data. Especially, we study how deep fully convolutional networks can be adapted to deal with multi-modal and multi-scale remote sensing data for semantic labeling. Our contributions are threefold: (a) we present an efficient multi-scale approach to leverage both a large spatial context and the high resolution data, (b) we investigate early and late fusion of Lidar and multispectral data, (c) we validate our methods on two public datasets with state-of-the-art results. Our results indicate that late fusion make it possible to recover errors steaming from ambiguous data, while early fusion allows for better joint-feature learning but at the cost of higher sensitivity to missing data.

Extraction and Analysis of Major Autumn Crops in Jingxian County Based on Multi - Temporal gf - 1 Remote Sensing Image and Object-Oriented

NASA Astrophysics Data System (ADS)

Ren, B.; Wen, Q.; Zhou, H.; Guan, F.; Li, L.; Yu, H.; Wang, Z.

2018-04-01

The purpose of this paper is to provide decision support for the adjustment and optimization of crop planting structure in Jingxian County. The object-oriented information extraction method is used to extract corn and cotton from Jingxian County of Hengshui City in Hebei Province, based on multi-period GF-1 16-meter images. The best time of data extraction was screened by analyzing the spectral characteristics of corn and cotton at different growth stages based on multi-period GF-116-meter images, phenological data, and field survey data. The results showed that the total classification accuracy of corn and cotton was up to 95.7 %, the producer accuracy was 96 % and 94 % respectively, and the user precision was 95.05 % and 95.9 % respectively, which satisfied the demand of crop monitoring application. Therefore, combined with multi-period high-resolution images and object-oriented classification can be a good extraction of large-scale distribution of crop information for crop monitoring to provide convenient and effective technical means.

Geospatial Information from Satellite Imagery for Geovisualisation of Smart Cities in India

NASA Astrophysics Data System (ADS)

Mohan, M.

2016-06-01

In the recent past, there have been large emphasis on extraction of geospatial information from satellite imagery. The Geospatial information are being processed through geospatial technologies which are playing important roles in developing of smart cities, particularly in developing countries of the world like India. The study is based on the latest geospatial satellite imagery available for the multi-date, multi-stage, multi-sensor, and multi-resolution. In addition to this, the latest geospatial technologies have been used for digital image processing of remote sensing satellite imagery and the latest geographic information systems as 3-D GeoVisualisation, geospatial digital mapping and geospatial analysis for developing of smart cities in India. The Geospatial information obtained from RS and GPS systems have complex structure involving space, time and presentation. Such information helps in 3-Dimensional digital modelling for smart cities which involves of spatial and non-spatial information integration for geographic visualisation of smart cites in context to the real world. In other words, the geospatial database provides platform for the information visualisation which is also known as geovisualisation. So, as a result there have been an increasing research interest which are being directed to geospatial analysis, digital mapping, geovisualisation, monitoring and developing of smart cities using geospatial technologies. However, the present research has made an attempt for development of cities in real world scenario particulary to help local, regional and state level planners and policy makers to better understand and address issues attributed to cities using the geospatial information from satellite imagery for geovisualisation of Smart Cities in emerging and developing country, India.

Choosing a Global Positioning System Device for Use in U.S. Army Corps of Engineers Regulatory Districts

DTIC Science & Technology

2017-12-01

Information Systems Center of Expertise (RS/GIS CX) (CEERD-RZR), U.S. Army Engineer Research and Development Center, Cold Regions Research and...GIS Geographic Information Systems GPS Global Positioning System HH Handheld IWR U.S. Army Engineer Institute for Water Resources n/a Not...Applicable NAE U.S. Army New England Regulatory District RS/GIS Remote Sensing/Geographic Information Systems SD Secure Digital SDHC Secure Digital High

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

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

NASA Technical Reports Server (NTRS)

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

2008-01-01

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

On the Integration of Remote Experimentation into Undergraduate Laboratories--Pedagogical Approach

ERIC Educational Resources Information Center

Esche, Sven K.

2005-01-01

This paper presents an Internet-based open approach to laboratory instruction. In this article, the author talks about an open laboratory approach using a multi-user multi-device remote facility. This approach involves both the direct contact with the computer-controlled laboratory setup of interest with the students present in the laboratory…

Components of spatial information management in wildlife ecology: Software for statistical and modeling analysis [Chapter 14

Treesearch

Hawthorne L. Beyer; Jeff Jenness; Samuel A. Cushman

2010-01-01

Spatial information systems (SIS) is a term that describes a wide diversity of concepts, techniques, and technologies related to the capture, management, display and analysis of spatial information. It encompasses technologies such as geographic information systems (GIS), global positioning systems (GPS), remote sensing, and relational database management systems (...

Multi-frequency communication system and method

DOEpatents

Carrender, Curtis Lee; Gilbert, Ronald W.

2004-06-01

A multi-frequency RFID remote communication system is provided that includes a plurality of RFID tags configured to receive a first signal and to return a second signal, the second signal having a first frequency component and a second frequency component, the second frequency component including data unique to each remote RFID tag. The system further includes a reader configured to transmit an interrogation signal and to receive remote signals from the tags. A first signal processor, preferably a mixer, removes an intermediate frequency component from the received signal, and a second processor, preferably a second mixer, analyzes the IF frequency component to output data that is unique to each remote tag.

The Physics of Imaging with Remote Sensors : Photon State Space & Radiative Transfer

NASA Technical Reports Server (NTRS)

Davis, Anthony B.

2012-01-01

Standard (mono-pixel/steady-source) retrieval methodology is reaching its fundamental limit with access to multi-angle/multi-spectral photo- polarimetry. Next... Two emerging new classes of retrieval algorithm worth nurturing: multi-pixel time-domain Wave-radiometry transition regimes, and more... Cross-fertilization with bio-medical imaging. Physics-based remote sensing: - What is "photon state space?" - What is "radiative transfer?" - Is "the end" in sight? Two wide-open frontiers! center dot Examples (with variations.

Multi-sources data fusion framework for remote triage prioritization in telehealth.

PubMed

Salman, O H; Rasid, M F A; Saripan, M I; Subramaniam, S K

2014-09-01

The healthcare industry is streamlining processes to offer more timely and effective services to all patients. Computerized software algorithm and smart devices can streamline the relation between users and doctors by providing more services inside the healthcare telemonitoring systems. This paper proposes a multi-sources framework to support advanced healthcare applications. The proposed framework named Multi Sources Healthcare Architecture (MSHA) considers multi-sources: sensors (ECG, SpO2 and Blood Pressure) and text-based inputs from wireless and pervasive devices of Wireless Body Area Network. The proposed framework is used to improve the healthcare scalability efficiency by enhancing the remote triaging and remote prioritization processes for the patients. The proposed framework is also used to provide intelligent services over telemonitoring healthcare services systems by using data fusion method and prioritization technique. As telemonitoring system consists of three tiers (Sensors/ sources, Base station and Server), the simulation of the MSHA algorithm in the base station is demonstrated in this paper. The achievement of a high level of accuracy in the prioritization and triaging patients remotely, is set to be our main goal. Meanwhile, the role of multi sources data fusion in the telemonitoring healthcare services systems has been demonstrated. In addition to that, we discuss how the proposed framework can be applied in a healthcare telemonitoring scenario. Simulation results, for different symptoms relate to different emergency levels of heart chronic diseases, demonstrate the superiority of our algorithm compared with conventional algorithms in terms of classify and prioritize the patients remotely.

Global hierarchical classification of deepwater and wetland environments from remote sensing products

NASA Astrophysics Data System (ADS)

Fluet-Chouinard, E.; Lehner, B.; Aires, F.; Prigent, C.; McIntyre, P. B.

2017-12-01

Global surface water maps have improved in spatial and temporal resolutions through various remote sensing methods: open water extents with compiled Landsat archives and inundation with topographically downscaled multi-sensor retrievals. These time-series capture variations through time of open water and inundation without discriminating between hydrographic features (e.g. lakes, reservoirs, river channels and wetland types) as other databases have done as static representation. Available data sources present the opportunity to generate a comprehensive map and typology of aquatic environments (deepwater and wetlands) that improves on earlier digitized inventories and maps. The challenge of classifying surface waters globally is to distinguishing wetland types with meaningful characteristics or proxies (hydrology, water chemistry, soils, vegetation) while accommodating limitations of remote sensing data. We present a new wetland classification scheme designed for global application and produce a map of aquatic ecosystem types globally using state-of-the-art remote sensing products. Our classification scheme combines open water extent and expands it with downscaled multi-sensor inundation data to capture the maximal vegetated wetland extent. The hierarchical structure of the classification is modified from the Cowardin Systems (1979) developed for the USA. The first level classification is based on a combination of landscape positions and water source (e.g. lacustrine, riverine, palustrine, coastal and artificial) while the second level represents the hydrologic regime (e.g. perennial, seasonal, intermittent and waterlogged). Class-specific descriptors can further detail the wetland types with soils and vegetation cover. Our globally consistent nomenclature and top-down mapping allows for direct comparison across biogeographic regions, to upscale biogeochemical fluxes as well as other landscape level functions.

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.

Tools and Data Services from the NASA Earth Satellite Observations for Remote Sensing Commercial Applications

NASA Technical Reports Server (NTRS)

Vicente, Gilberto

2005-01-01

Several commercial applications of remote sensing data, such as water resources management, environmental monitoring, climate prediction, agriculture, forestry, preparation for and migration of extreme weather events, require access to vast amounts of archived high quality data, software tools and services for data manipulation and information extraction. These on the other hand require gaining detailed understanding of the data's internal structure and physical implementation of data reduction, combination and data product production. The time-consuming task must be undertaken before the core investigation can begin and is an especially difficult challenge when science objectives require users to deal with large multi-sensor data sets of different formats, structures, and resolutions.

A computer software system for integration and analysis of grid-based remote sensing data with other natural resource data. Remote Sensing Project

NASA Technical Reports Server (NTRS)

Tilmann, S. E.; Enslin, W. R.; Hill-Rowley, R.

1977-01-01

A computer-based information system is described designed to assist in the integration of commonly available spatial data for regional planning and resource analysis. The Resource Analysis Program (RAP) provides a variety of analytical and mapping phases for single factor or multi-factor analyses. The unique analytical and graphic capabilities of RAP are demonstrated with a study conducted in Windsor Township, Eaton County, Michigan. Soil, land cover/use, topographic and geological maps were used as a data base to develope an eleven map portfolio. The major themes of the portfolio are land cover/use, non-point water pollution, waste disposal, and ground water recharge.

Research and Application of Remote Sensing Monitoring Method for Desertification Land Under Time and Space Constraints

NASA Astrophysics Data System (ADS)

Zhang, Nannnan; Wang, Rongbao; Zhang, Feng

2018-04-01

Serious land desertification and sandified threaten the urban ecological security and the sustainable economic and social development. In recent years, a large number of mobile sand dunes in Horqin sandy land flow into the northwest of Liaoning Province under the monsoon, make local agriculture suffer serious harm. According to the characteristics of desertification land in northwestern Liaoning, based on the First National Geographical Survey data, the Second National Land Survey data and the 1984-2014 Landsat satellite long time sequence data and other multi-source data, we constructed a remote sensing monitoring index system of desertification land in Northwest Liaoning. Through the analysis of space-time-spectral characteristics of desertification land, a method for multi-spectral remote sensing image recognition of desertification land under time-space constraints is proposed. This method was used to identify and extract the distribution and classification of desertification land of Chaoyang City (a typical citie of desertification in northwestern Liaoning) in 2008 and 2014, and monitored the changes and transfers of desertification land from 2008 to 2014. Sandification information was added to the analysis of traditional landscape changes, improved the analysis model of desertification land landscape index, and the characteristics and laws of landscape dynamics and landscape pattern change of desertification land from 2008 to 2014 were analyzed and revealed.

[Spatial-temporal evolution characterization of land subsidence by multi-temporal InSAR method and GIS technology].

PubMed

Chen, Bei-Bei; Gong, Hui-Li; Li, Xiao-Juan; Lei, Kun-Chao; Duan, Guang-Yao; Xie, Jin-Rong

2014-04-01

Long-term over-exploitation of underground resources, and static and dynamic load increase year by year influence the occurrence and development of regional land subsidence to a certain extent. Choosing 29 scenes Envisat ASAR images covering plain area of Beijing, China, the present paper used the multi-temporal InSAR method incorporating both persistent scatterer and small baseline approaches, and obtained monitoring information of regional land subsidence. Under different situation of space development and utilization, the authors chose five typical settlement areas; With classified information of land-use, multi-spectral remote sensing image, and geological data, and adopting GIS spatial analysis methods, the authors analyzed the time series evolution characteristics of uneven settlement. The comprehensive analysis results suggests that the complex situations of space development and utilization affect the trend of uneven settlement; the easier the situation of space development and utilization, the smaller the settlement gradient, and the less the uneven settlement trend.

End-to-End Information System design at the NASA Jet Propulsion Laboratory

NASA Technical Reports Server (NTRS)

Hooke, A. J.

1978-01-01

Recognizing a pressing need of the 1980s to optimize the two-way flow of information between a ground-based user and a remote space-based sensor, an end-to-end approach to the design of information systems has been adopted at the Jet Propulsion Laboratory. The objectives of this effort are to ensure that all flight projects adequately cope with information flow problems at an early stage of system design, and that cost-effective, multi-mission capabilities are developed when capital investments are made in supporting elements. The paper reviews the End-to-End Information System (EEIS) activity at the Laboratory, and notes the ties to the NASA End-to-End Data System program.

Multi-frequency entanglement router system

NASA Astrophysics Data System (ADS)

Erdmann, Reinhard; Hughes, David

2017-05-01

A high performance free-space Wavelength Division Multiplexed (WDM) transceiver system is assessed as to its viability for routing collinear entangled photons in place of the classical optical signals for which it was designed. Explicit calculations demonstrate that entanglement in the input state is retained through transit of the system without intrinsic loss. Introducing spatial degrees of freedom changed the entanglement so that it could be manifested at remote locations, as required in non-local Bell test measurements or Quantum Key Distribution (QKD) Protocols. It was also found that by adding proper components, the exit state could be changed from being frequency entangled to polarization entangled, with respect to the (remote) paths of the photons. Finally it was found possible to route a complete entangled state to either of the two remote users by proper selection of the discrete frequencies in the input state. Each entanglement in the photon states was maximal, hence suited for Quantum Information Processing (QIP) applications.

[Ecosystem services evaluation based on geographic information system and remote sensing technology: a review].

PubMed

Li, Wen-Jie; Zhang, Shi-Huang; Wang, Hui-Min

2011-12-01

Ecosystem services evaluation is a hot topic in current ecosystem management, and has a close link with human beings welfare. This paper summarized the research progress on the evaluation of ecosystem services based on geographic information system (GIS) and remote sensing (RS) technology, which could be reduced to the following three characters, i. e., ecological economics theory is widely applied as a key method in quantifying ecosystem services, GIS and RS technology play a key role in multi-source data acquisition, spatiotemporal analysis, and integrated platform, and ecosystem mechanism model becomes a powerful tool for understanding the relationships between natural phenomena and human activities. Aiming at the present research status and its inadequacies, this paper put forward an "Assembly Line" framework, which was a distributed one with scalable characteristics, and discussed the future development trend of the integration research on ecosystem services evaluation based on GIS and RS technologies.

Study on multispectral imaging detection and recognition

NASA Astrophysics Data System (ADS)

Jun, Wang; Na, Ding; Gao, Jiaobo; Yu, Hu; Jun, Wu; Li, Junna; Zheng, Yawei; Fei, Gao; Sun, Kefeng

2009-07-01

Multispectral imaging detecting technology use target radiation character in spectral spatial distribution and relation between spectral and image to detect target and remote sensing measure. Its speciality is multi channel, narrow bandwidth, large amount of information, high accuracy. The ability of detecting target in environment of clutter, camouflage, concealment and beguilement is improved. At present, spectral imaging technology in the range of multispectral and hyperspectral develop greatly. The multispectral imaging equipment of unmanned aerial vehicle can be used in mine detection, information, surveillance and reconnaissance. Spectral imaging spectrometer operating in MWIR and LWIR has already been applied in the field of remote sensing and military in the advanced country. The paper presents the technology of multispectral imaging. It can enhance the reflectance, scatter and radiation character of the artificial targets among nature background. The targets among complex background and camouflage/stealth targets can be effectively identified. The experiment results and the data of spectral imaging is obtained.

Particle swarm optimization using multi-information characteristics of all personal-best information.

PubMed

Huang, Song; Tian, Na; Wang, Yan; Ji, Zhicheng

2016-01-01

Convergence stagnation is the chief difficulty to solve hard optimization problems for most particle swarm optimization variants. To address this issue, a novel particle swarm optimization using multi-information characteristics of all personal-best information is developed in our research. In the modified algorithm, two positions are defined by personal-best positions and an improved cognition term with three positions of all personal-best information is used in velocity update equation to enhance the search capability. This strategy could make particles fly to a better direction by discovering useful information from all the personal-best positions. The validity of the proposed algorithm is assessed on twenty benchmark problems including unimodal, multimodal, rotated and shifted functions, and the results are compared with that obtained by some published variants of particle swarm optimization in the literature. Computational results demonstrate that the proposed algorithm finds several global optimum and high-quality solutions in most case with a fast convergence speed.

Integration and Field Trials of a High-Resolution Multi-beam Sonar on the Remote Mine hunting Vehicle Dorado

DTIC Science & Technology

2003-12-01

Minehunting System (RMS), is a semi-submersible, remotely controlled drone designed to tow an actively stabilized sidescan sonar towfish. The multi... comparativement aux véhicules sous-marins autonomes, ils offrent le positionnement DGPS, la commande en temps réel et la télémesure, en plus...minehunting vehicle. The Reson 8125 multi-beam bathymetric sonar is designed to acquire high-resolution (of order cm) bathymetry in a 240- beam swath 120

[Small unmanned aerial vehicles for low-altitude remote sensing and its application progress in ecology.

PubMed

Sun, Zhong Yu; Chen, Yan Qiao; Yang, Long; Tang, Guang Liang; Yuan, Shao Xiong; Lin, Zhi Wen

2017-02-01

Low-altitude unmanned aerial vehicles (UAV) remote sensing system overcomes the deficiencies of space and aerial remote sensing system in resolution, revisit period, cloud cover and cost, which provides a novel method for ecological research on mesoscale. This study introduced the composition of UAV remote sensing system, reviewed its applications in species, population, community and ecosystem ecology research. Challenges and opportunities of UAV ecology were identified to direct future research. The promising research area of UAV ecology includes the establishment of species morphology and spectral characteristic data base, species automatic identification, the revelation of relationship between spectral index and plant physiological processes, three-dimension monitoring of ecosystem, and the integration of remote sensing data from multi resources and multi scales. With the development of UAV platform, data transformation and sensors, UAV remote sensing technology will have wide application in ecology research.

Remote Sensing of Aerosol and their Radiative Forcing of Climate

NASA Technical Reports Server (NTRS)

Kaufman, Yoram J.; Tanre, Didier; Remer, Lorraine A.

1999-01-01

Remote sensing of aerosol and aerosol radiative forcing of climate is going through a major transformation. The launch in next few years of new satellites designed specifically for remote sensing of aerosol is expected to further revolutionized aerosol measurements: until five years ago satellites were not designed for remote sensing of aerosol. Aerosol optical thickness was derived as a by product, only over the oceans using one AVHRR channel with errors of approx. 50%. However it already revealed a very important first global picture of the distribution and sources of aerosol. In the last 5 years we saw the introduction of polarization and multi-view observations (POLDER and ATSR) for satellite remote sensing of aerosol over land and ocean. Better products are derived from AVHRR using its two channels. The new TOMS aerosol index shows the location and transport of aerosol over land and ocean. Now we anticipate the launch of EOS-Terra with MODIS, MISR and CERES on board for multi-view, multi-spectral remote sensing of aerosol and its radiative forcing. This will allow application of new techniques, e.g. using a wide spectral range (0.55-2.2 microns) to derive precise optical thickness, particle size and mass loading. Aerosol is transparent in the 2.2 microns channel, therefore this channel can be used to detect surface features that in turn are used to derive the aerosol optical thickness in the visible part of the spectrum. New techniques are developed to derive the aerosol single scattering albedo, a measure of absorption of sunlight, and techniques to derive directly the aerosol forcing at the top of the atmosphere. In the last 5 years a global network of sun/sky radiometers was formed, designed to communicate in real time the spectral optical thickness from 50-80 locations every day, every 15 minutes. The sky angular and spectral information is also measured and used to retrieve the aerosol size distribution, refractive index, single scattering albedo and the spectral flux reaching the surface. Effort to introduce remote sensing from lidars will literally additional dimension to aerosol remote sensing. The vertical dimension is a critical link between the global satellite observations and modeling of aerosol transport. Lidars are also critical to study aerosol impact on cloud microphysics and reflectance. Both lidar ground networks and satellite systems are in development. This new capability is expected to put remote sensing in the forefront of aerosol and climate studies. Together with field experiments, chemical analysis and chemical transport models we anticipate, in the next decade, to be able to resolve some of the outstanding questions regarding the role of aerosol in climate, in atmospheric chemistry and its influence on human health and life on this planet.

Method and source for producing a high concentration of positively charged molecular hydrogen or deuterium ions

DOEpatents

Ehlers, Kenneth W.; Leung, Ka-Ngo

1988-01-01

A high concentration of positive molecular ions of hydrogen or deuterium gas is extracted from a positive ion source having a short path length of extracted ions, relative to the mean free path of the gas molecules, to minimize the production of other ion species by collision between the positive ions and gas molecules. The ion source has arrays of permanent magnets to produce a multi-cusp magnetic field in regions remote from the plasma grid and the electron emitters, for largely confining the plasma to the space therebetween. The ion source has a chamber which is short in length, relative to its transverse dimensions, and the electron emitters are at an even shorter distance from the plasma grid, which contains one or more extraction apertures.

An Indoor Slam Method Based on Kinect and Multi-Feature Extended Information Filter

NASA Astrophysics Data System (ADS)

Chang, M.; Kang, Z.

2017-09-01

Based on the frame of ORB-SLAM in this paper the transformation parameters between adjacent Kinect image frames are computed using ORB keypoints, from which priori information matrix and information vector are calculated. The motion update of multi-feature extended information filter is then realized. According to the point cloud data formed by depth image, ICP algorithm was used to extract the point features of the point cloud data in the scene and built an observation model while calculating a-posteriori information matrix and information vector, and weakening the influences caused by the error accumulation in the positioning process. Furthermore, this paper applied ORB-SLAM frame to realize autonomous positioning in real time in interior unknown environment. In the end, Lidar was used to get data in the scene in order to estimate positioning accuracy put forward in this paper.

Study on environment detection and appraisement of mining area with RS

NASA Astrophysics Data System (ADS)

Yang, Fengjie; Hou, Peng; Zhou, Guangzhu; Li, Qingting; Wang, Jie; Cheng, Jianguang

2006-12-01

In this paper, the big coal mining area Yanzhou is selected as the typical research area. According to the special dynamic change characteristic of the environment in the mining area, the environmental dynamic changes are timely monitored with the remote sensing detection technology. Environmental special factors, such as vegetation, water, air, land-over, are extracted by the professional remote sensing image processing software, then the spatial information is managed and analyzed in the geographical information system (GIS) software. As the result, the dynamic monitor and query for change information is achieved, and the special environmental factor dynamic change maps are protracted. On the base of the data coming from the remote sensing image, GIS and the traditional environment monitoring, the environmental quality is appraised with the method of indistinct matrix analysis, the multi-index and the analytical hierarchy process. At last, those provide the credible science foundation for the local environment appraised and the sustained development. In addition, this paper apply the hyper spectrum graphs by the FieldSpec Pro spectroradiometer, together with the analytical data from environmental chemical, to study the growth of vegetation which were seed in the land-over consisting of gangue, which is a new method to study the impact to vegetation that are growing in the soil.

An environmental scan of emergency response systems and services in remote First Nations communities in Northern Ontario.

PubMed

Mew, E J; Ritchie, S D; VanderBurgh, D; Beardy, J L; Gordon, J; Fortune, M; Mamakwa, S; Orkin, A M

2017-01-01

Approximately 24,000 Ontarians live in remote Indigenous communities with no road access. These communities are a subset of Nishnawbe Aski Nation (NAN), a political grouping of 49 First Nations communities in Northern Ontario, Canada. Limited information is available regarding the status of emergency care in these communities. We aimed to understand emergency response systems, services, and training in remote NAN communities. We used an environmental scan approach to compile information from multiple sources including community-based participatory research. This included the analysis of data collected from key informant interviews (n=10) with First Nations community health leaders and a multi-stakeholder roundtable meeting (n=33) in October 2013. Qualitative analysis of the interview data revealed four issues related to emergency response systems and training: (1) inequity in response capacity and services, (2) lack of formalised dispatch systems, (3) turnover and burnout in volunteer emergency services, and (4) challenges related to first aid training. Roundtable stakeholders supported the development of a community-based emergency care system to address gaps. Existing first response, paramedical, and ambulance service models do not meet the unique geographical, epidemiological and cultural needs in most NAN communities. Sustainable, context-appropriate, and culturally relevant emergency care systems are needed.

A medical application integrating remote 3D visualization tools to access picture archiving and communication system on mobile devices.

PubMed

He, Longjun; Ming, Xing; Liu, Qian

2014-04-01

With computing capability and display size growing, the mobile device has been used as a tool to help clinicians view patient information and medical images anywhere and anytime. However, for direct interactive 3D visualization, which plays an important role in radiological diagnosis, the mobile device cannot provide a satisfactory quality of experience for radiologists. This paper developed a medical system that can get medical images from the picture archiving and communication system on the mobile device over the wireless network. In the proposed application, the mobile device got patient information and medical images through a proxy server connecting to the PACS server. Meanwhile, the proxy server integrated a range of 3D visualization techniques, including maximum intensity projection, multi-planar reconstruction and direct volume rendering, to providing shape, brightness, depth and location information generated from the original sectional images for radiologists. Furthermore, an algorithm that changes remote render parameters automatically to adapt to the network status was employed to improve the quality of experience. Finally, performance issues regarding the remote 3D visualization of the medical images over the wireless network of the proposed application were also discussed. The results demonstrated that this proposed medical application could provide a smooth interactive experience in the WLAN and 3G networks.

Assessing Wetland Hydroperiod and Soil Moisture with Remote Sensing: A Demonstration for the NASA Plum Brook Station Year 2

NASA Technical Reports Server (NTRS)

Brooks, Colin; Bourgeau-Chavez, Laura; Endres, Sarah; Battaglia, Michael; Shuchman, Robert

2015-01-01

Assist with the evaluation and measuring of wetlands hydroperiod at the Plum Brook Station using multi-source remote sensing data as part of a larger effort on projecting climate change-related impacts on the station's wetland ecosystems. MTRI expanded on the multi-source remote sensing capabilities to help estimate and measure hydroperiod and the relative soil moisture of wetlands at NASA's Plum Brook Station. Multi-source remote sensing capabilities are useful in estimating and measuring hydroperiod and relative soil moisture of wetlands. This is important as a changing regional climate has several potential risks for wetland ecosystem function. The year two analysis built on the first year of the project by acquiring and analyzing remote sensing data for additional dates and types of imagery, combined with focused field work. Five deliverables were planned and completed: (1) Show the relative length of hydroperiod using available remote sensing datasets, (2) Date linked table of wetlands extent over time for all feasible non-forested wetlands, (3) Utilize LIDAR data to measure topographic height above sea level of all wetlands, wetland to catchment area radio, slope of wetlands, and other useful variables (4), A demonstration of how analyzed results from multiple remote sensing data sources can help with wetlands vulnerability assessment; and (5) A MTRI style report summarizing year 2 results.

Hyperspectral and Radar Airborne Imagery over Controlled Release of Oil at Sea.

PubMed

Angelliaume, Sébastien; Ceamanos, Xavier; Viallefont-Robinet, Françoise; Baqué, Rémi; Déliot, Philippe; Miegebielle, Véronique

2017-08-02

Remote sensing techniques are commonly used by Oil and Gas companies to monitor hydrocarbon on the ocean surface. The interest lies not only in exploration but also in the monitoring of the maritime environment. Occurrence of natural seeps on the sea surface is a key indicator of the presence of mature source rock in the subsurface. These natural seeps, as well as the oil slicks, are commonly detected using radar sensors but the addition of optical imagery can deliver extra information such as thickness and composition of the detected oil, which is critical for both exploration purposes and efficient cleanup operations. Today, state-of-the-art approaches combine multiple data collected by optical and radar sensors embedded on-board different airborne and spaceborne platforms, to ensure wide spatial coverage and high frequency revisit time. Multi-wavelength imaging system may create a breakthrough in remote sensing applications, but it requires adapted processing techniques that need to be developed. To explore performances offered by multi-wavelength radar and optical sensors for oil slick monitoring, remote sensing data have been collected by SETHI (Système Expérimental de Télédection Hyperfréquence Imageur), the airborne system developed by ONERA (the French Aerospace Lab), during an oil spill cleanup exercise carried out in 2015 in the North Sea, Europe. The uniqueness of this dataset lies in its high spatial resolution, low noise level and quasi-simultaneous acquisitions of different part of the EM spectrum. Specific processing techniques have been developed to extract meaningful information associated with oil-covered sea surface. Analysis of this unique and rich dataset demonstrates that remote sensing imagery, collected in both optical and microwave domains, allows estimating slick surface properties such as the age of the emulsion released at sea, the spatial abundance of oil and the relative concentration of hydrocarbons remaining on the sea surface.

Hyperspectral and Radar Airborne Imagery over Controlled Release of Oil at Sea

PubMed Central

Angelliaume, Sébastien; Ceamanos, Xavier; Viallefont-Robinet, Françoise; Baqué, Rémi; Déliot, Philippe

2017-01-01

Remote sensing techniques are commonly used by Oil and Gas companies to monitor hydrocarbon on the ocean surface. The interest lies not only in exploration but also in the monitoring of the maritime environment. Occurrence of natural seeps on the sea surface is a key indicator of the presence of mature source rock in the subsurface. These natural seeps, as well as the oil slicks, are commonly detected using radar sensors but the addition of optical imagery can deliver extra information such as thickness and composition of the detected oil, which is critical for both exploration purposes and efficient cleanup operations. Today, state-of-the-art approaches combine multiple data collected by optical and radar sensors embedded on-board different airborne and spaceborne platforms, to ensure wide spatial coverage and high frequency revisit time. Multi-wavelength imaging system may create a breakthrough in remote sensing applications, but it requires adapted processing techniques that need to be developed. To explore performances offered by multi-wavelength radar and optical sensors for oil slick monitoring, remote sensing data have been collected by SETHI (Système Expérimental de Télédection Hyperfréquence Imageur), the airborne system developed by ONERA (the French Aerospace Lab), during an oil spill cleanup exercise carried out in 2015 in the North Sea, Europe. The uniqueness of this dataset lies in its high spatial resolution, low noise level and quasi-simultaneous acquisitions of different part of the EM spectrum. Specific processing techniques have been developed to extract meaningful information associated with oil-covered sea surface. Analysis of this unique and rich dataset demonstrates that remote sensing imagery, collected in both optical and microwave domains, allows estimating slick surface properties such as the age of the emulsion released at sea, the spatial abundance of oil and the relative concentration of hydrocarbons remaining on the sea surface. PMID:28767059

Impact of user influence on information multi-step communication in a micro-blog

NASA Astrophysics Data System (ADS)

Wu, Yue; Hu, Yong; He, Xiao-Hai; Deng, Ken

2014-06-01

User influence is generally considered as one of the most critical factors that affect information cascading spreading. Based on this common assumption, this paper proposes a theoretical model to examine user influence on the information multi-step communication in a micro-blog. The multi-steps of information communication are divided into first-step and non-first-step, and user influence is classified into five dimensions. Actual data from the Sina micro-blog is collected to construct the model by means of an approach based on structural equations that uses the Partial Least Squares (PLS) technique. Our experimental results indicate that the dimensions of the number of fans and their authority significantly impact the information of first-step communication. Leader rank has a positive impact on both first-step and non-first-step communication. Moreover, global centrality and weight of friends are positively related to the information non-first-step communication, but authority is found to have much less relation to it.

Early warning of illegal development for protected areas by integrating cellular automata with neural networks.

PubMed

Li, Xia; Lao, Chunhua; Liu, Yilun; Liu, Xiaoping; Chen, Yimin; Li, Shaoying; Ai, Bing; He, Zijian

2013-11-30

Ecological security has become a major issue under fast urbanization in China. As the first two cities in this country, Shenzhen and Dongguan issued the ordinance of Eco-designated Line of Control (ELC) to "wire" ecologically important areas for strict protection in 2005 and 2009 respectively. Early warning systems (EWS) are a useful tool for assisting the implementation ELC. In this study, a multi-model approach is proposed for the early warning of illegal development by integrating cellular automata (CA) and artificial neural networks (ANN). The objective is to prevent the ecological risks or catastrophe caused by such development at an early stage. The integrated model is calibrated by using the empirical information from both remote sensing and handheld GPS (global positioning systems). The MAR indicator which is the ratio of missing alarms to all the warnings is proposed for better assessment of the model performance. It is found that the fast urban development has caused significant threats to natural-area protection in the study area. The integration of CA, ANN and GPS provides a powerful tool for describing and predicting illegal development which is in highly non-linear and fragmented forms. The comparison shows that this multi-model approach has much better performances than the single-model approach for the early warning. Compared with the single models of CA and ANN, this integrated multi-model can improve the value of MAR by 65.48% and 5.17% respectively. Copyright © 2013 Elsevier Ltd. All rights reserved.

Effects of bathymetric lidar errors on flow properties predicted with a multi-dimensional hydraulic model

Treesearch

J. McKean; D. Tonina; C. Bohn; C. W. Wright

2014-01-01

New remote sensing technologies and improved computer performance now allow numerical flow modeling over large stream domains. However, there has been limited testing of whether channel topography can be remotely mapped with accuracy necessary for such modeling. We assessed the ability of the Experimental Advanced Airborne Research Lidar, to support a multi-dimensional...

Real-Time and Post-Processed Georeferencing for Hyperpspectral Drone Remote Sensing

NASA Astrophysics Data System (ADS)

Oliveira, R. A.; Khoramshahi, E.; Suomalainen, J.; Hakala, T.; Viljanen, N.; Honkavaara, E.

2018-05-01

The use of drones and photogrammetric technologies are increasing rapidly in different applications. Currently, drone processing workflow is in most cases based on sequential image acquisition and post-processing, but there are great interests towards real-time solutions. Fast and reliable real-time drone data processing can benefit, for instance, environmental monitoring tasks in precision agriculture and in forest. Recent developments in miniaturized and low-cost inertial measurement systems and GNSS sensors, and Real-time kinematic (RTK) position data are offering new perspectives for the comprehensive remote sensing applications. The combination of these sensors and light-weight and low-cost multi- or hyperspectral frame sensors in drones provides the opportunity of creating near real-time or real-time remote sensing data of target object. We have developed a system with direct georeferencing onboard drone to be used combined with hyperspectral frame cameras in real-time remote sensing applications. The objective of this study is to evaluate the real-time georeferencing comparing with post-processing solutions. Experimental data sets were captured in agricultural and forested test sites using the system. The accuracy of onboard georeferencing data were better than 0.5 m. The results showed that the real-time remote sensing is promising and feasible in both test sites.

[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.

Using distances between Top-n-gram and residue pairs for protein remote homology detection.

PubMed

Liu, Bin; Xu, Jinghao; Zou, Quan; Xu, Ruifeng; Wang, Xiaolong; Chen, Qingcai

2014-01-01

Protein remote homology detection is one of the central problems in bioinformatics, which is important for both basic research and practical application. Currently, discriminative methods based on Support Vector Machines (SVMs) achieve the state-of-the-art performance. Exploring feature vectors incorporating the position information of amino acids or other protein building blocks is a key step to improve the performance of the SVM-based methods. Two new methods for protein remote homology detection were proposed, called SVM-DR and SVM-DT. SVM-DR is a sequence-based method, in which the feature vector representation for protein is based on the distances between residue pairs. SVM-DT is a profile-based method, which considers the distances between Top-n-gram pairs. Top-n-gram can be viewed as a profile-based building block of proteins, which is calculated from the frequency profiles. These two methods are position dependent approaches incorporating the sequence-order information of protein sequences. Various experiments were conducted on a benchmark dataset containing 54 families and 23 superfamilies. Experimental results showed that these two new methods are very promising. Compared with the position independent methods, the performance improvement is obvious. Furthermore, the proposed methods can also provide useful insights for studying the features of protein families. The better performance of the proposed methods demonstrates that the position dependant approaches are efficient for protein remote homology detection. Another advantage of our methods arises from the explicit feature space representation, which can be used to analyze the characteristic features of protein families. The source code of SVM-DT and SVM-DR is available at http://bioinformatics.hitsz.edu.cn/DistanceSVM/index.jsp.

3D Cloud Tomography, Followed by Mean Optical and Microphysical Properties, with Multi-Angle/Multi-Pixel Data

NASA Astrophysics Data System (ADS)

Davis, A. B.; von Allmen, P. A.; Marshak, A.; Bal, G.

2010-12-01

The geometrical assumption in all operational cloud remote sensing algorithms is that clouds are plane-parallel slabs, which applies relatively well to the most uniform stratus layers. Its benefit is to justify using classic 1D radiative transfer (RT) theory, where angular details (solar, viewing, azimuthal) are fully accounted for and precise phase functions can be used, to generate the look-up tables used in the retrievals. Unsurprisingly, these algorithms catastrophically fail when applied to cumulus-type clouds, which are highly 3D. This is unfortunate for the cloud-process modeling community that may thrive on in situ airborne data, but would very much like to use satellite data for more than illustrations in their presentations and publications. So, how can we obtain quantitative information from space-based observations of finite aspect ratio clouds? Cloud base/top heights, vertically projected area, mean liquid water content (LWC), and volume-averaged droplet size would be a good start. Motivated by this science need, we present a new approach suitable for sparse cumulus fields where we turn the tables on the standard procedure in cloud remote sensing. We make no a priori assumption about cloud shape, save an approximately flat base, but use brutal approximations about the RT that is necessarily 3D. Indeed, the first order of business is to roughly determine the cloud's outer shape in one of two ways, which we will frame as competing initial guesses for the next phase of shape refinement and volume-averaged microphysical parameter estimation. Both steps use multi-pixel/multi-angle techniques amenable to MISR data, the latter adding a bi-spectral dimension using collocated MODIS data. One approach to rough cloud shape determination is to fit the multi-pixel/multi-angle data with a geometric primitive such as a scalene hemi-ellipsoid with 7 parameters (translation in 3D space, 3 semi-axes, 1 azimuthal orientation); for the radiometry, a simple radiosity-type model is used where the cloud surface "emits" either reflected (sunny-side) or transmitted (shady-side) light at different levels. As it turns out, the reflected/transmitted light ratio yields an approximate cloud optical thickness. Another approach is to invoke tomography techniques to define the volume occupied by the cloud using, as it were, cloud masks for each direction of observation. In the shape and opacity refinement phase, initial guesses along with solar and viewing geometry information are used to predict radiance in each pixel using a fast diffusion model for the 3D RT in MISR's non-absorbing red channel (275 m resolution). Refinement is constrained and stopped when optimal resolution is reached. Finally, multi-pixel/mono-angle MODIS data for the same cloud (at comparable 250 m resolution) reveals the desired droplet size information, hence the volume-averaged LWC. This is an ambitious remote sensing science project drawing on cross-disciplinary expertise gained in medical imaging using both X-ray and near-IR sources and detectors. It is high risk but with potentially high returns not only for the cloud modeling community but also aerosol and surface characterization in the presence of broken 3D clouds.

How Should Remote Clinical Monitoring Be Used to Treat Alcohol Use Disorders?: Initial Findings From an Expert Round Table Discussion.

PubMed

Gordon, Alan; Jaffe, Adi; McLellan, A Thomas; Richardson, Gary; Skipper, Gregory; Sucher, Michel; Tirado, Carlos F; Urschel, Harold C

Scientific evidence combined with new health insurance coverage now enable a chronic illness management approach to the treatment of alcohol use disorders (AUDs), including regular monitoring of blood alcohol content (BAC), as a useful indicator of disease control. Recent technical advances now permit many different types of remote, real-time monitoring of BAC. However, there is no body of research to empirically guide clinicians in how to maximize the clinical potential of remote BAC monitoring.As an initial step in guiding and supporting such research, the manufacturer of one remote BAC monitoring system sponsored a group of experienced clinicians and clinical researchers to discuss 8 issues that generally affect remote, clinical BAC monitoring of "adults in outpatient AUD treatment."The expert panel unanimously agreed that remote BAC monitoring for at least 12 months during and after the outpatient treatment of AUD was a clinically viable deterrent to relapse. There was also consensus that positive test results (ie, recent alcohol use) should lead to intensified care and monitoring. However, there was no agreement on specific types of clinical intensification after a positive test. The panel agreed that sharing positive and negative test results with members of the patient support group was helpful in reinforcing abstinence, yet they noted many practical issues regarding information sharing that remain concerning. Significant differences within the panel on several important clinical issues underline the need for more clinical and implementation research to produce empirically-supported guidelines for the use of remote BAC monitoring in AUD treatment.

How Should Remote Clinical Monitoring Be Used to Treat Alcohol Use Disorders?: Initial Findings From an Expert Round Table Discussion

PubMed Central

Gordon, Alan; Jaffe, Adi; McLellan, A. Thomas; Richardson, Gary; Skipper, Gregory; Sucher, Michel; Tirado, Carlos F.; Urschel, Harold C.

2017-01-01

Scientific evidence combined with new health insurance coverage now enable a chronic illness management approach to the treatment of alcohol use disorders (AUDs), including regular monitoring of blood alcohol content (BAC), as a useful indicator of disease control. Recent technical advances now permit many different types of remote, real-time monitoring of BAC. However, there is no body of research to empirically guide clinicians in how to maximize the clinical potential of remote BAC monitoring. As an initial step in guiding and supporting such research, the manufacturer of one remote BAC monitoring system sponsored a group of experienced clinicians and clinical researchers to discuss 8 issues that generally affect remote, clinical BAC monitoring of “adults in outpatient AUD treatment.” The expert panel unanimously agreed that remote BAC monitoring for at least 12 months during and after the outpatient treatment of AUD was a clinically viable deterrent to relapse. There was also consensus that positive test results (ie, recent alcohol use) should lead to intensified care and monitoring. However, there was no agreement on specific types of clinical intensification after a positive test. The panel agreed that sharing positive and negative test results with members of the patient support group was helpful in reinforcing abstinence, yet they noted many practical issues regarding information sharing that remain concerning. Significant differences within the panel on several important clinical issues underline the need for more clinical and implementation research to produce empirically-supported guidelines for the use of remote BAC monitoring in AUD treatment. PMID:28157829

Multi-decadal Arctic sea ice roughness.

NASA Astrophysics Data System (ADS)

Tsamados, M.; Stroeve, J.; Kharbouche, S.; Muller, J. P., , Prof; Nolin, A. W.; Petty, A.; Haas, C.; Girard-Ardhuin, F.; Landy, J.

2017-12-01

The transformation of Arctic sea ice from mainly perennial, multi-year ice to a seasonal, first-year ice is believed to have been accompanied by a reduction of the roughness of the ice cover surface. This smoothening effect has been shown to (i) modify the momentum and heat transfer between the atmosphere and ocean, (ii) to alter the ice thickness distribution which in turn controls the snow and melt pond repartition over the ice cover, and (iii) to bias airborne and satellite remote sensing measurements that depend on the scattering and reflective characteristics over the sea ice surface topography. We will review existing and novel remote sensing methodologies proposed to estimate sea ice roughness, ranging from airborne LIDAR measurement (ie Operation IceBridge), to backscatter coefficients from scatterometers (ASCAT, QUICKSCAT), to multi angle maging spectroradiometer (MISR), and to laser (Icesat) and radar altimeters (Envisat, Cryosat, Altika, Sentinel-3). We will show that by comparing and cross-calibrating these different products we can offer a consistent multi-mission, multi-decadal view of the declining sea ice roughness. Implications for sea ice physics, climate and remote sensing will also be discussed.

Reactions to a remote-controlled video-communication robot in seniors' homes: a pilot study of feasibility and acceptance.

PubMed

Seelye, Adriana M; Wild, Katherine V; Larimer, Nicole; Maxwell, Shoshana; Kearns, Peter; Kaye, Jeffrey A

2012-12-01

Remote telepresence provided by tele-operated robotics represents a new means for obtaining important health information, improving older adults' social and daily functioning and providing peace of mind to family members and caregivers who live remotely. In this study we tested the feasibility of use and acceptance of a remotely controlled robot with video-communication capability in independently living, cognitively intact older adults. A mobile remotely controlled robot with video-communication ability was placed in the homes of eight seniors. The attitudes and preferences of these volunteers and those of family or friends who communicated with them remotely via the device were assessed through survey instruments. Overall experiences were consistently positive, with the exception of one user who subsequently progressed to a diagnosis of mild cognitive impairment. Responses from our participants indicated that in general they appreciated the potential of this technology to enhance their physical health and well-being, social connectedness, and ability to live independently at home. Remote users, who were friends or adult children of the participants, were more likely to test the mobility features and had several suggestions for additional useful applications. Results from the present study showed that a small sample of independently living, cognitively intact older adults and their remote collaterals responded positively to a remote controlled robot with video-communication capabilities. Research is needed to further explore the feasibility and acceptance of this type of technology with a variety of patients and their care contacts.

Remote Viewer for Maritime Robotics Software

NASA Technical Reports Server (NTRS)

Kuwata, Yoshiaki; Wolf, Michael; Huntsberger, Terrance L.; Howard, Andrew B.

2013-01-01

This software is a viewer program for maritime robotics software that provides a 3D visualization of the boat pose, its position history, ENC (Electrical Nautical Chart) information, camera images, map overlay, and detected tracks.

Deterministic Teleportation of Multi-qudit States in a Network via Various Probabilistic Channels

NASA Astrophysics Data System (ADS)

Zhang, Ti-Hang; Jiang, Min; Huang, Xu; Wan, Min

2014-04-01

In this paper, we present a generalized approach to faithfully teleport an unknown state of a multi-qudit system involving multi spatially remote agents via various probabilistic channels. In a quantum teleportation network, there are generally multi spatially remote relay agents between a sender and a distant receiver. With the assistance of the relay agents, it is possible to directly construct a deterministic channel between the sender and the distant receiver. In our scheme, different from previous probabilistic teleportation protocols, the integrity of the unknown multi-qudit state could be maintained even when the construction of faithful channel fails. Our results also show that the required auxiliary particle resources, local operations and classical communications are considerably reduced for the present purpose.

Optimal Configuration and Deployment of Software on Multi-Core Processing Architectures

DTIC Science & Technology

2008-07-01

between the event generating threads and the collector thread is implemented through semaphores . The Perseus data logger is designed to minimize the...performance counters (through the PAPI API) and opens up access to the shared memory logger through a semaphore and Remote Procedure Call (RPC) buffer... synchronization events. Using this rich data, the TMAM is able to output all of the information necessary to identify precisely which pairs of thread

Proof of Concept Integration of a Single-Level Service-Oriented Architecture into a Multi-Domain Secure Environment

DTIC Science & Technology

2008-03-01

Machine [29]. OC4J applications support Java Servlets , Web services, and the following J2EE specific standards: Extensible Markup Language (XML...IMAP Internet Message Access Protocol IP Internet Protocol IT Information Technology xviii J2EE Java Enterprise Environment JSR 168 Java ...LDAP), World Wide Web Distributed Authoring and Versioning (WebDav), Java Specification Request 168 (JSR 168), and Web Services for Remote

50 Mb/s, 220-mW Laser-Array Transmitter

NASA Technical Reports Server (NTRS)

Cornwell, Donald M., Jr.

1992-01-01

Laser transmitter based on injection locking produces single-wavelength, diffraction-limited, single-lobe beam. Output stage is array of laser diodes producing non-diffraction-limited, multi-mode beam in absence of injection locking. Suitable for both free-space and optical-fiber communication systems. Because beam from transmitter focused to spot as small as 5 micrometers, devices usable for reading and writing optical disks at increased information densities. Application also in remote sensing and ranging.

A Multi-Disciplinary Approach to Remote Sensing through Low-Cost UAVs.

PubMed

Calvario, Gabriela; Sierra, Basilio; Alarcón, Teresa E; Hernandez, Carmen; Dalmau, Oscar

2017-06-16

The use of Unmanned Aerial Vehicles (UAVs) based on remote sensing has generated low cost monitoring, since the data can be acquired quickly and easily. This paper reports the experience related to agave crop analysis with a low cost UAV. The data were processed by traditional photogrammetric flow and data extraction techniques were applied to extract new layers and separate the agave plants from weeds and other elements of the environment. Our proposal combines elements of photogrammetry, computer vision, data mining, geomatics and computer science. This fusion leads to very interesting results in agave control. This paper aims to demonstrate the potential of UAV monitoring in agave crops and the importance of information processing with reliable data flow.

System and method for inventorying multiple remote objects

DOEpatents

Carrender, Curtis L.; Gilbert, Ronald W.

2007-10-23

A system and method of inventorying multiple objects utilizing a multi-level or a chained radio frequency identification system. The system includes a master tag and a plurality of upper level tags and lower level tags associated with respective objects. The upper and lower level tags communicate with each other and the master tag so that reading of the master tag reveals the presence and absence of upper and lower level tags. In the chained RF system, the upper and lower level tags communicate locally with each other in a manner so that more remote tags that are out of range of some of the upper and lower level tags have their information relayed through adjacent tags to the master tag and thence to a controller.

System and method for inventorying multiple remote objects

DOEpatents

Carrender, Curtis L [Morgan Hill, CA; Gilbert, Ronald W [Morgan Hill, CA

2009-12-29

A system and method of inventorying multiple objects utilizing a multi-level or a chained radio frequency identification system. The system includes a master tag and a plurality of upper level tags and lower level tags associated with respective objects. The upper and lower level tags communicate with each other and the master tag so that reading of the master tag reveals the presence and absence of upper and lower level tags. In the chained RF system, the upper and lower level tags communicate locally with each other in a manner so that more remote tags that are out of range of some of the upper and lower level tags have their information relayed through adjacent tags to the master tag and thence to a controller.

Efficient Remote Preparation of Four-Qubit Cluster-Type Entangled States with Multi-Party Over Partially Entangled Channels

NASA Astrophysics Data System (ADS)

Wang, Dong; Hoehn, Ross D.; Ye, Liu; Kais, Sabre

2016-07-01

We present a strategy for realizing multiparty-controlled remote state preparation (MCRSP) for a family of four-qubit cluster-type states by taking a pair of partial entanglements as the quantum channels. In this scenario, the encoded information is transmitted from the sender to a spatially separated receiver with control of the transmission by multiple parties. Predicated on the collaboration of all participants, the desired state can be faithfully restored at the receiver's location with high success probability by application of additional appropriate local operations and necessary classical communication. Moreover, this proposal for MCRSP can be faithfully achieved with unit total success probability when the quantum channels are distilled to maximally entangled ones.

A Multi-Disciplinary Approach to Remote Sensing through Low-Cost UAVs

PubMed Central

Calvario, Gabriela; Sierra, Basilio; Alarcón, Teresa E.; Hernandez, Carmen; Dalmau, Oscar

2017-01-01

The use of Unmanned Aerial Vehicles (UAVs) based on remote sensing has generated low cost monitoring, since the data can be acquired quickly and easily. This paper reports the experience related to agave crop analysis with a low cost UAV. The data were processed by traditional photogrammetric flow and data extraction techniques were applied to extract new layers and separate the agave plants from weeds and other elements of the environment. Our proposal combines elements of photogrammetry, computer vision, data mining, geomatics and computer science. This fusion leads to very interesting results in agave control. This paper aims to demonstrate the potential of UAV monitoring in agave crops and the importance of information processing with reliable data flow. PMID:28621740

Airborne multicamera system for geo-spatial applications

NASA Astrophysics Data System (ADS)

Bachnak, Rafic; Kulkarni, Rahul R.; Lyle, Stacey; Steidley, Carl W.

2003-08-01

Airborne remote sensing has many applications that include vegetation detection, oceanography, marine biology, geographical information systems, and environmental coastal science analysis. Remotely sensed images, for example, can be used to study the aftermath of episodic events such as the hurricanes and floods that occur year round in the coastal bend area of Corpus Christi. This paper describes an Airborne Multi-Spectral Imaging System that uses digital cameras to provide high resolution at very high rates. The software is based on Delphi 5.0 and IC Imaging Control's ActiveX controls. Both time and the GPS coordinates are recorded. Three successful test flights have been conducted so far. The paper present flight test results and discusses the issues being addressed to fully develop the system.

Neural networks for satellite remote sensing and robotic sensor interpretation

NASA Astrophysics Data System (ADS)

Martens, Siegfried

Remote sensing of forests and robotic sensor fusion can be viewed, in part, as supervised learning problems, mapping from sensory input to perceptual output. This dissertation develops ARTMAP neural networks for real-time category learning, pattern recognition, and prediction tailored to remote sensing and robotics applications. Three studies are presented. The first two use ARTMAP to create maps from remotely sensed data, while the third uses an ARTMAP system for sensor fusion on a mobile robot. The first study uses ARTMAP to predict vegetation mixtures in the Plumas National Forest based on spectral data from the Landsat Thematic Mapper satellite. While most previous ARTMAP systems have predicted discrete output classes, this project develops new capabilities for multi-valued prediction. On the mixture prediction task, the new network is shown to perform better than maximum likelihood and linear mixture models. The second remote sensing study uses an ARTMAP classification system to evaluate the relative importance of spectral and terrain data for map-making. This project has produced a large-scale map of remotely sensed vegetation in the Sierra National Forest. Network predictions are validated with ground truth data, and maps produced using the ARTMAP system are compared to a map produced by human experts. The ARTMAP Sierra map was generated in an afternoon, while the labor intensive expert method required nearly a year to perform the same task. The robotics research uses an ARTMAP system to integrate visual information and ultrasonic sensory information on a B14 mobile robot. The goal is to produce a more accurate measure of distance than is provided by the raw sensors. ARTMAP effectively combines sensory sources both within and between modalities. The improved distance percept is used to produce occupancy grid visualizations of the robot's environment. The maps produced point to specific problems of raw sensory information processing and demonstrate the benefits of using a neural network system for sensor fusion.

Ocean Color Measurements from Landsat-8 OLI using SeaDAS

NASA Technical Reports Server (NTRS)

Franz, Bryan Alden; Bailey, Sean W.; Kuring, Norman; Werdell, P. Jeremy

2014-01-01

The Operational Land Imager (OLI) is a multi-spectral radiometer hosted on the recently launched Landsat-8 satellite. OLI includes a suite of relatively narrow spectral bands at 30-meter spatial resolution in the visible to shortwave infrared that make it a potential tool for ocean color radiometry: measurement of the reflected spectral radiance upwelling from beneath the ocean surface that carries information on the biogeochemical constituents of the upper ocean euphotic zone. To evaluate the potential of OLI to measure ocean color, processing support was implemented in SeaDAS, which is an open-source software package distributed by NASA for processing, analysis, and display of ocean remote sensing measurements from a variety of satellite-based multi-spectral radiometers. Here we describe the implementation of OLI processing capabilities within SeaDAS, including support for various methods of atmospheric correction to remove the effects of atmospheric scattering and absorption and retrieve the spectral remote-sensing reflectance (Rrs; sr exp 1). The quality of the retrieved Rrs imagery will be assessed, as will the derived water column constituents such as the concentration of the phytoplankton pigment chlorophyll a.

Efficiency analysis for 3D filtering of multichannel images

NASA Astrophysics Data System (ADS)

Kozhemiakin, Ruslan A.; Rubel, Oleksii; Abramov, Sergey K.; Lukin, Vladimir V.; Vozel, Benoit; Chehdi, Kacem

2016-10-01

Modern remote sensing systems basically acquire images that are multichannel (dual- or multi-polarization, multi- and hyperspectral) where noise, usually with different characteristics, is present in all components. If noise is intensive, it is desirable to remove (suppress) it before applying methods of image classification, interpreting, and information extraction. This can be done using one of two approaches - by component-wise or by vectorial (3D) filtering. The second approach has shown itself to have higher efficiency if there is essential correlation between multichannel image components as this often happens for multichannel remote sensing data of different origin. Within the class of 3D filtering techniques, there are many possibilities and variations. In this paper, we consider filtering based on discrete cosine transform (DCT) and pay attention to two aspects of processing. First, we study in detail what changes in DCT coefficient statistics take place for 3D denoising compared to component-wise processing. Second, we analyze how selection of component images united into 3D data array influences efficiency of filtering and can the observed tendencies be exploited in processing of images with rather large number of channels.

A two-step framework for reconstructing remotely sensed land surface temperatures contaminated by cloud

NASA Astrophysics Data System (ADS)

Zeng, Chao; Long, Di; Shen, Huanfeng; Wu, Penghai; Cui, Yaokui; Hong, Yang

2018-07-01

Land surface temperature (LST) is one of the most important parameters in land surface processes. Although satellite-derived LST can provide valuable information, the value is often limited by cloud contamination. In this paper, a two-step satellite-derived LST reconstruction framework is proposed. First, a multi-temporal reconstruction algorithm is introduced to recover invalid LST values using multiple LST images with reference to corresponding remotely sensed vegetation index. Then, all cloud-contaminated areas are temporally filled with hypothetical clear-sky LST values. Second, a surface energy balance equation-based procedure is used to correct for the filled values. With shortwave irradiation data, the clear-sky LST is corrected to the real LST under cloudy conditions. A series of experiments have been performed to demonstrate the effectiveness of the developed approach. Quantitative evaluation results indicate that the proposed method can recover LST in different surface types with mean average errors in 3-6 K. The experiments also indicate that the time interval between the multi-temporal LST images has a greater impact on the results than the size of the contaminated area.

Image Mining in Remote Sensing for Coastal Wetlands Mapping: from Pixel Based to Object Based Approach

NASA Astrophysics Data System (ADS)

Farda, N. M.; Danoedoro, P.; Hartono; Harjoko, A.

2016-11-01

The availably of remote sensing image data is numerous now, and with a large amount of data it makes “knowledge gap” in extraction of selected information, especially coastal wetlands. Coastal wetlands provide ecosystem services essential to people and the environment. The aim of this research is to extract coastal wetlands information from satellite data using pixel based and object based image mining approach. Landsat MSS, Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI images located in Segara Anakan lagoon are selected to represent data at various multi temporal images. The input for image mining are visible and near infrared bands, PCA band, invers PCA bands, mean shift segmentation bands, bare soil index, vegetation index, wetness index, elevation from SRTM and ASTER GDEM, and GLCM (Harralick) or variability texture. There is three methods were applied to extract coastal wetlands using image mining: pixel based - Decision Tree C4.5, pixel based - Back Propagation Neural Network, and object based - Mean Shift segmentation and Decision Tree C4.5. The results show that remote sensing image mining can be used to map coastal wetlands ecosystem. Decision Tree C4.5 can be mapped with highest accuracy (0.75 overall kappa). The availability of remote sensing image mining for mapping coastal wetlands is very important to provide better understanding about their spatiotemporal coastal wetlands dynamics distribution.

A novel linear physical model for remote sensing of snow wetness and snow density using the visible and infrared bands

NASA Astrophysics Data System (ADS)

Varade, D. M.; Dikshit, O.

2017-12-01

Modeling and forecasting of snowmelt runoff are significant for understanding the hydrological processes in the cryosphere which requires timely information regarding snow physical properties such as liquid water content and density of snow in the topmost layer of the snowpack. Both the seasonal runoffs and avalanche forecasting are vastly dependent on the inherent physical characteristics of the snowpack which are conventionally measured by field surveys in difficult terrains at larger impending costs and manpower. With advances in remote sensing technology and the increase in the availability of satellite data, the frequency and extent of these surveys could see a declining trend in future. In this study, we present a novel approach for estimating snow wetness and snow density using visible and infrared bands that are available with most multi-spectral sensors. We define a trapezoidal feature space based on the spectral reflectance in the near infrared band and the Normalized Differenced Snow Index (NDSI), referred to as NIR-NDSI space, where dry snow and wet snow are observed in the left diagonal upper and lower right corners, respectively. The corresponding pixels are extracted by approximating the dry and wet edges which are used to develop a linear physical model to estimate snow wetness. Snow density is then estimated using the modeled snow wetness. Although the proposed approach has used Sentinel-2 data, it can be extended to incorporate data from other multi-spectral sensors. The estimated values for snow wetness and snow density show a high correlation with respect to in-situ measurements. The proposed model opens a new avenue for remote sensing of snow physical properties using multi-spectral data, which were limited in the literature.

Using Multi-Temporal Remote Sensing Data to Analyze the Spatio-Temporal Patterns of Dry Season Rice Production in Bangladesh

NASA Astrophysics Data System (ADS)

Shew, A. M.; Ghosh, A.

2017-10-01

Remote sensing in the optical domain is widely used in agricultural monitoring; however, such initiatives pose a challenge for developing countries due to a lack of high quality in situ information. Our proposed methodology could help developing countries bridge this gap by demonstrating the potential to quantify patterns of dry season rice production in Bangladesh. To analyze approximately 90,000 km2 of cultivated land in Bangladesh at 30 m spatial resolution, we used two decades of remote sensing data from the Landsat archive and Google Earth Engine (GEE), a cloud-based geospatial data analysis platform built on Google infrastructure and capable of processing petabyte-scale remote sensing data. We reconstructed the seasonal patterns of vegetation indices (VIs) for each pixel using a harmonic time series (HTS) model, which minimizes the effects of missing observations and noise. Next, we combined the seasonality information of VIs with our knowledge of rice cultivation systems in Bangladesh to delineate rice areas in the dry season, which are predominantly hybrid and High Yielding Varieties (HYV). Based on historical Landsat imagery, the harmonic time series of vegetation indices (HTS-VIs) model estimated 4.605 million ha, 3.519 million ha, and 4.021 million ha of rice production for Bangladesh in 2005, 2010, and 2015 respectively. Fine spatial scale information on HYV rice over the last 20 years will greatly improve our understanding of double-cropped rice systems, current status of production, and potential for HYV rice adoption in Bangladesh during the dry season.

Position-aware deep multi-task learning for drug-drug interaction extraction.

PubMed

Zhou, Deyu; Miao, Lei; He, Yulan

2018-05-01

A drug-drug interaction (DDI) is a situation in which a drug affects the activity of another drug synergistically or antagonistically when being administered together. The information of DDIs is crucial for healthcare professionals to prevent adverse drug events. Although some known DDIs can be found in purposely-built databases such as DrugBank, most information is still buried in scientific publications. Therefore, automatically extracting DDIs from biomedical texts is sorely needed. In this paper, we propose a novel position-aware deep multi-task learning approach for extracting DDIs from biomedical texts. In particular, sentences are represented as a sequence of word embeddings and position embeddings. An attention-based bidirectional long short-term memory (BiLSTM) network is used to encode each sentence. The relative position information of words with the target drugs in text is combined with the hidden states of BiLSTM to generate the position-aware attention weights. Moreover, the tasks of predicting whether or not two drugs interact with each other and further distinguishing the types of interactions are learned jointly in multi-task learning framework. The proposed approach has been evaluated on the DDIExtraction challenge 2013 corpus and the results show that with the position-aware attention only, our proposed approach outperforms the state-of-the-art method by 0.99% for binary DDI classification, and with both position-aware attention and multi-task learning, our approach achieves a micro F-score of 72.99% on interaction type identification, outperforming the state-of-the-art approach by 1.51%, which demonstrates the effectiveness of the proposed approach. Copyright © 2018 Elsevier B.V. All rights reserved.

Development of Decision Support System for Remote Monitoring of PIP Corn

EPA Science Inventory

The EPA is developing a multi-level approach that utilizes satellite and airborne remote sensing to locate and monitor genetically modified corn in the agricultural landscape and pest infestation. The current status of the EPA IRM monitoring program based on remote sensed imager...

[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.

Bottom-up assessment of the Net Ecosystem Carbon Balance of Russian forests in 2010 for comparison to Top-down estimates.

NASA Astrophysics Data System (ADS)

Maksyutov, S. S.; Shvidenko, A.; Shchepashchenko, D.

2014-12-01

The verified full carbon assessment of Russian forests (FCA) is based on an Integrated Land Information System (ILIS) that includes a multi-layer and multi-scale GIS with basic resolution of 1 km and corresponding attributive databases. The ILIS aggregates all available information about ecosystems and landscapes, sets of empirical and semi-empirical data and aggregations, data of different inventories and surveys, and multi-sensor remote sensing data. The ILIS serves as an information base for application of the landscape-ecosystem approach (LEA) of the FCA and as a systems design for comparison and mutual constraints with other methods of study of carbon cycling of forest ecosystems (eddy covariance; process models; inverse modeling; and multi-sensor application of remote sensing). The LEA is based on a complimentary use of the flux-based method with some elements of the pool-based method. Introduction of climatic parameters of individual years in the LEA, as well as some process-based elements, allows providing a substantial decrease of the uncertainties of carbon cycling yearly indicators of forest ecosystems. Major carbon pools (live biomass, coarse woody debris, soil organic carbon) are estimated based on data on areas, distribution and major biometric characteristics of Russian forests presented in form of the ILIS for the country. The major fluxes accounted for include Net Primary Production (NPP), Soil Heterotrophic Respiration (SHR), as well as fluxes caused by decomposition of Coarse Woody Debris (CWD), harvest and use of forest products, fluxes caused by natural disturbances (fire, insect outbreaks, impacts of unfavorable environment) and lateral fluxes to hydrosphere and lithosphere. Use of landscape-ecosystem approach resulted in the NECB at 573±140 Tg C yr-1 (CI 0.9). While the total carbon sink is high, large forest areas, particularly on permafrost, serve as a carbon source. The ratio between net primary production and soil heterotrophic respiration, together with natural and human-induced disturbances are major drivers of the magnitude and spatial distribution of the NECB of forest ecosystems. We also present comparison to the recent top-down estimates of the Siberian carbon sink.

Remote sensing-based characterization of rainfall during atmospheric rivers over the central United States

NASA Astrophysics Data System (ADS)

Nayak, Munir A.; Villarini, Gabriele

2018-01-01

Atmospheric rivers (ARs) play a central role in the hydrology and hydroclimatology of the central United States. More than 25% of the annual rainfall is associated with ARs over much of this region, with many large flood events tied to their occurrence. Despite the relevance of these storms for flood hydrology and water budget, the characteristics of rainfall associated with ARs over the central United has not been investigated thus far. This study fills this major scientific gap by describing the rainfall during ARs over the central United States using five remote sensing-based precipitation products over a 12-year study period. The products we consider are: Stage IV, Tropical Rainfall Measuring Mission - Multi-satellite Precipitation Analysis (TMPA, both real-time and research version); Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN); the CPC MORPHing Technique (CMORPH). As part of the study, we evaluate these products against a rain gauge-based dataset using both graphical- and metrics-based diagnostics. Based on our analyses, Stage IV is found to better reproduce the reference data. Hence, we use it for the characterization of rainfall in ARs. Most of the AR-rainfall is located in a narrow region within ∼150 km on both sides of the AR major axis. In this region, rainfall has a pronounced positive relationship with the magnitude of the water vapor transport. Moreover, we have also identified a consistent increase in rainfall intensity with duration (or persistence) of AR conditions. However, there is not a strong indication of diurnal variability in AR rainfall. These results can be directly used in developing flood protection strategies during ARs. Further, weather prediction agencies can benefit from the results of this study to achieve higher skill of resolving precipitation processes in their models.

WaveJava: Wavelet-based network computing

NASA Astrophysics Data System (ADS)

Ma, Kun; Jiao, Licheng; Shi, Zhuoer

1997-04-01

Wavelet is a powerful theory, but its successful application still needs suitable programming tools. Java is a simple, object-oriented, distributed, interpreted, robust, secure, architecture-neutral, portable, high-performance, multi- threaded, dynamic language. This paper addresses the design and development of a cross-platform software environment for experimenting and applying wavelet theory. WaveJava, a wavelet class library designed by the object-orient programming, is developed to take advantage of the wavelets features, such as multi-resolution analysis and parallel processing in the networking computing. A new application architecture is designed for the net-wide distributed client-server environment. The data are transmitted with multi-resolution packets. At the distributed sites around the net, these data packets are done the matching or recognition processing in parallel. The results are fed back to determine the next operation. So, the more robust results can be arrived quickly. The WaveJava is easy to use and expand for special application. This paper gives a solution for the distributed fingerprint information processing system. It also fits for some other net-base multimedia information processing, such as network library, remote teaching and filmless picture archiving and communications.

Background suppression of infrared small target image based on inter-frame registration

NASA Astrophysics Data System (ADS)

Ye, Xiubo; Xue, Bindang

2018-04-01

We propose a multi-frame background suppression method for remote infrared small target detection. Inter-frame information is necessary when the heavy background clutters make it difficult to distinguish real targets and false alarms. A registration procedure based on points matching in image patches is used to compensate the local deformation of background. Then the target can be separated by background subtraction. Experiments show our method serves as an effective preliminary of target detection.

Expectations of medical specialists about image-based teleconsultation - A qualitative study on acute burns in South Africa.

PubMed

Blom, Lisa; Laflamme, Lucie; Mölsted Alvesson, Helle

2018-01-01

Image-based teleconsultation between medical experts and healthcare staff at remote emergency centres can improve the diagnosis of conditions which are challenging to assess. One such condition is burns. Knowledge is scarce regarding how medical experts perceive the influence of such teleconsultation on their roles and relations to colleagues at point of care. In this qualitative study, semi-structured interviews were conducted with 15 medical experts to explore their expectations of a newly developed App for burns diagnostics and care prior to its implementation. Purposive sampling included male and female physicians at different stages of their career, employed at different referral hospitals and all potential future tele-experts in remote teleconsultation using the App. Positioning theory was used to analyse the data. The experts are already facing changes in their diagnostic practices due to the informal use of open access applications like WhatsApp. Additional changes are expected when the new App is launched. Four positions of medical experts were identified in situations of diagnostic advice, two related to patient flow-clinical specialist and gatekeeper-and two to point of care staff-educator and mentor. The experts move flexibly between the positions during diagnostic practices with remote colleagues. A new position in relation to previous research on medical roles-the mentor-came to light in this setting. The App is expected to have an important educational impact, streamline the diagnostic process, improve both triage and referrals and be a more secure option for remote diagnosis compared to current practices. Verbal communication is however expected to remain important for certain situations, in particular those related to the mentor position. The quality and security of referrals are expected to be improved through the App but the medical experts see less potential for conveying moral support via the App during remote consultations. Experts' reflections on remote consultations highlight the embedded social and cultural dimensions of implementing new technology.

Expectations of medical specialists about image-based teleconsultation – A qualitative study on acute burns in South Africa

PubMed Central

Laflamme, Lucie; Mölsted Alvesson, Helle

2018-01-01

Background Image-based teleconsultation between medical experts and healthcare staff at remote emergency centres can improve the diagnosis of conditions which are challenging to assess. One such condition is burns. Knowledge is scarce regarding how medical experts perceive the influence of such teleconsultation on their roles and relations to colleagues at point of care. Methods In this qualitative study, semi-structured interviews were conducted with 15 medical experts to explore their expectations of a newly developed App for burns diagnostics and care prior to its implementation. Purposive sampling included male and female physicians at different stages of their career, employed at different referral hospitals and all potential future tele-experts in remote teleconsultation using the App. Positioning theory was used to analyse the data. Results The experts are already facing changes in their diagnostic practices due to the informal use of open access applications like WhatsApp. Additional changes are expected when the new App is launched. Four positions of medical experts were identified in situations of diagnostic advice, two related to patient flow–clinical specialist and gatekeeper–and two to point of care staff–educator and mentor. The experts move flexibly between the positions during diagnostic practices with remote colleagues. A new position in relation to previous research on medical roles–the mentor–came to light in this setting. The App is expected to have an important educational impact, streamline the diagnostic process, improve both triage and referrals and be a more secure option for remote diagnosis compared to current practices. Verbal communication is however expected to remain important for certain situations, in particular those related to the mentor position. Conclusion The quality and security of referrals are expected to be improved through the App but the medical experts see less potential for conveying moral support via the App during remote consultations. Experts’ reflections on remote consultations highlight the embedded social and cultural dimensions of implementing new technology. PMID:29543847

Groundwater assessment in Salboni Block, West Bengal (India) using remote sensing, geographical information system and multi-criteria decision analysis techniques

NASA Astrophysics Data System (ADS)

Jha, Madan K.; Chowdary, V. M.; Chowdhury, Alivia

2010-11-01

An approach is presented for the evaluation of groundwater potential using remote sensing, geographic information system, geoelectrical, and multi-criteria decision analysis techniques. The approach divides the available hydrologic and hydrogeologic data into two groups, exogenous (hydrologic) and endogenous (subsurface). A case study in Salboni Block, West Bengal (India), uses six thematic layers of exogenous parameters and four thematic layers of endogenous parameters. These thematic layers and their features were assigned suitable weights which were normalized by analytic hierarchy process and eigenvector techniques. The layers were then integrated using ArcGIS software to generate two groundwater potential maps. The hydrologic parameters-based groundwater potential zone map indicated that the `good' groundwater potential zone covers 27.14% of the area, the `moderate' zone 45.33%, and the `poor' zone 27.53%. A comparison of this map with the groundwater potential map based on subsurface parameters revealed that the hydrologic parameters-based map accurately delineates groundwater potential zones in about 59% of the area, and hence it is dependable to a certain extent. More than 80% of the study area has moderate-to-poor groundwater potential, which necessitates efficient groundwater management for long-term water security. Overall, the integrated technique is useful for the assessment of groundwater resources at a basin or sub-basin scale.

The information extraction of Gannan citrus orchard based on the GF-1 remote sensing image

NASA Astrophysics Data System (ADS)

Wang, S.; Chen, Y. L.

2017-02-01

The production of Gannan oranges is the largest in China, which occupied an important part in the world. The extraction of citrus orchard quickly and effectively has important significance for fruit pathogen defense, fruit production and industrial planning. The traditional spectra extraction method of citrus orchard based on pixel has a lower classification accuracy, difficult to avoid the “pepper phenomenon”. In the influence of noise, the phenomenon that different spectrums of objects have the same spectrum is graveness. Taking Xunwu County citrus fruit planting area of Ganzhou as the research object, aiming at the disadvantage of the lower accuracy of the traditional method based on image element classification method, a decision tree classification method based on object-oriented rule set is proposed. Firstly, multi-scale segmentation is performed on the GF-1 remote sensing image data of the study area. Subsequently the sample objects are selected for statistical analysis of spectral features and geometric features. Finally, combined with the concept of decision tree classification, a variety of empirical values of single band threshold, NDVI, band combination and object geometry characteristics are used hierarchically to execute the information extraction of the research area, and multi-scale segmentation and hierarchical decision tree classification is implemented. The classification results are verified with the confusion matrix, and the overall Kappa index is 87.91%.

Remote sensing for industrial applications in the energy business: digital territorial data integration for planning of overhead power transmission lines (OHTLs)

NASA Astrophysics Data System (ADS)

Terrazzino, Alfonso; Volponi, Silvia; Borgogno Mondino, Enrico

2001-12-01

An investigation has been carried out, concerning remote sensing techniques, in order to assess their potential application to the energy system business: the most interesting results concern a new approach, based on digital data from remote sensing, to infrastructures with a large territorial distribution: in particular OverHead Transmission Lines, for the high voltage transmission and distribution of electricity on large distances. Remote sensing could in principle be applied to all the phases of the system lifetime, from planning to design, to construction, management, monitoring and maintenance. In this article, a remote sensing based approach is presented, targeted to the line planning: optimization of OHTLs path and layout, according to different parameters (technical, environmental and industrial). Planning new OHTLs is of particular interest in emerging markets, where typically the cartography is missing or available only on low accuracy scale (1:50.000 and lower), often not updated. Multi- spectral images can be used to generate thematic maps of the region of interest for the planning (soil coverage). Digital Elevation Models (DEMs), allow the planners to easily access the morphologic information of the surface. Other auxiliary information from local laws, environmental instances, international (IEC) standards can be integrated in order to perform an accurate optimized path choice and preliminary spotting of the OHTLs. This operation is carried out by an ABB proprietary optimization algorithm: the output is a preliminary path that bests fits the optimization parameters of the line in a life cycle approach.

The Use of a Geographic Information System and Remote Sensing Technology for Monitoring Land Use and Soil Carbon Change in the Subtropical Dry Forest Life Zone of Puerto Rico

NASA Technical Reports Server (NTRS)

Velez-Rodriguez, Linda L. (Principal Investigator)

1996-01-01

Aerial photography, one of the first form of remote sensing technology, has long been an invaluable means to monitor activities and conditions at the Earth's surface. Geographic Information Systems or GIS is the use of computers in showing and manipulating spatial data. This report will present the use of geographic information systems and remote sensing technology for monitoring land use and soil carbon change in the subtropical dry forest life zone of Puerto Rico. This research included the south of Puerto Rico that belongs to the subtropical dry forest life zone. The Guanica Commonwealth Forest Biosphere Reserve and the Jobos Bay National Estuarine Research Reserve are studied in detail, because of their location in the subtropical dry forest life zone. Aerial photography, digital multispectral imagery, soil samples, soil survey maps, field inspections, and differential global positioning system (DGPS) observations were used.

Reactions to a Remote-Controlled Video-Communication Robot in Seniors' Homes: A Pilot Study of Feasibility and Acceptance

PubMed Central

Seelye, Adriana M.; Larimer, Nicole; Maxwell, Shoshana; Kearns, Peter; Kaye, Jeffrey A.

2012-01-01

Abstract Objective: Remote telepresence provided by tele-operated robotics represents a new means for obtaining important health information, improving older adults' social and daily functioning and providing peace of mind to family members and caregivers who live remotely. In this study we tested the feasibility of use and acceptance of a remotely controlled robot with video-communication capability in independently living, cognitively intact older adults. Materials and Methods: A mobile remotely controlled robot with video-communication ability was placed in the homes of eight seniors. The attitudes and preferences of these volunteers and those of family or friends who communicated with them remotely via the device were assessed through survey instruments. Results: Overall experiences were consistently positive, with the exception of one user who subsequently progressed to a diagnosis of mild cognitive impairment. Responses from our participants indicated that in general they appreciated the potential of this technology to enhance their physical health and well-being, social connectedness, and ability to live independently at home. Remote users, who were friends or adult children of the participants, were more likely to test the mobility features and had several suggestions for additional useful applications. Conclusions: Results from the present study showed that a small sample of independently living, cognitively intact older adults and their remote collaterals responded positively to a remote controlled robot with video-communication capabilities. Research is needed to further explore the feasibility and acceptance of this type of technology with a variety of patients and their care contacts. PMID:23082794

Geothermal Prospecting with Remote Sensing and Geographical Information System Technologies in Xilingol Volcanic Field in the Eastern Inner Mongolia, NE China

NASA Astrophysics Data System (ADS)

Peng, F.; Huang, S.; Xiong, Y.; Zhao, Y.; Cheng, Y.

2013-05-01

Geothermal energy is a renewable and low-carbon energy source independent of climate change. It is most abundant in Cenozoic volcanic areas where high temperature can be obtained within a relatively shallow depth. Like other geological resources, geothermal resource prospecting and exploration require a good understanding of the host media. Remote sensing (RS) has the advantages of high spatial and temporal resolution and broad spatial coverage over the conventional geological and geophysical prospecting, while geographical information system (GIS) has intuitive, flexible, and convenient characteristics. In this study, we apply RS and GIS technics in prospecting the geothermal energy potential in Xilingol, a Cenozoic volcanic field in the eastern Inner Mongolia, NE China. Landsat TM/ETM+ multi-temporal images taken under clear-sky conditions, digital elevation model (DEM) data, and other auxiliary data including geological maps of 1:2,500,000 and 1:200,000 scales are used in this study. The land surface temperature (LST) of the study area is retrieved from the Landsat images with the single-channel algorithm on the platform of ENVI developed by ITT Visual Information Solutions. Information of linear and circular geological structure is then extracted from the LST maps and compared to the existing geological data. Several useful technologies such as principal component analysis (PCA), vegetation suppression technique, multi-temporal comparative analysis, and 3D Surface View based on DEM data are used to further enable a better visual geologic interpretation with the Landsat imagery of Xilingol. The Preliminary results show that major faults in the study area are mainly NE and NNE oriented. Several major volcanism controlling faults and Cenozoic volcanic eruption centers have been recognized from the linear and circular structures in the remote images. Seven areas have been identified as potential targets for further prospecting geothermal energy based on the visual interpretation of the geological structures. The study shows that GIS and RS have great application potential in the geothermal exploration in volcanic areas and will promote the exploration of renewable energy resources of great potential.

Solid state remote circuit selector switch

NASA Technical Reports Server (NTRS)

Peterson, V. S.

1970-01-01

Remote switching circuit utilizes voltage logic to switch on desired circuit. Circuit controls rotating multi-range pressure transducers in jet engine testing and can be used in coded remote circuit activator where sequence of switching has to occur in defined length of time to prevent false or undesired circuit activation.

Development of a generalized algorithm of satellite remote sensing using multi-wavelength and multi-pixel information (MWP method) for aerosol properties by satellite-borne imager

NASA Astrophysics Data System (ADS)

Hashimoto, M.; Nakajima, T.; Morimoto, S.; Takenaka, H.

2014-12-01

We have developed a new satellite remote sensing algorithm to retrieve the aerosol optical characteristics using multi-wavelength and multi-pixel information of satellite imagers (MWP method). In this algorithm, the inversion method is a combination of maximum a posteriori (MAP) method (Rodgers, 2000) and the Phillips-Twomey method (Phillips, 1962; Twomey, 1963) as a smoothing constraint for the state vector. Furthermore, with the progress of computing technique, this method has being combined with the direct radiation transfer calculation numerically solved by each iteration step of the non-linear inverse problem, without using LUT (Look Up Table) with several constraints.Retrieved parameters in our algorithm are aerosol optical properties, such as aerosol optical thickness (AOT) of fine and coarse mode particles, a volume soot fraction in fine mode particles, and ground surface albedo of each observed wavelength. We simultaneously retrieve all the parameters that characterize pixels in each of horizontal sub-domains consisting the target area. Then we successively apply the retrieval method to all the sub-domains in the target area.We conducted numerical tests for the retrieval of aerosol properties and ground surface albedo for GOSAT/CAI imager data to test the algorithm for the land area. The result of the experiment showed that AOTs of fine mode and coarse mode, soot fraction and ground surface albedo are successfully retrieved within expected accuracy. We discuss the accuracy of the algorithm for various land surface types. Then, we applied this algorithm to GOSAT/CAI imager data, and we compared retrieved and surface-observed AOTs at the CAI pixel closest to an AERONET (Aerosol Robotic Network) or SKYNET site in each region. Comparison at several sites in urban area indicated that AOTs retrieved by our method are in agreement with surface-observed AOT within ±0.066.Our future work is to extend the algorithm for analysis of AGEOS-II/GLI and GCOM/C-SGLI data.

Twenty years of Landsat data accessible through the national satellite land remote sensing data archive

USGS Publications Warehouse

Larsen, Dana M.

1993-01-01

The EROS Data Center has managed to National Satellite Land Remote Sensing Data Archive's (NSLRSDA) Landsat data since 1972. The NSLRSDA includes Landsat MSS data from 1972 through 1991 and T M data from 1982 through 1993. In response to many requests from multi-disciplined users for an enhanced insight into the availability and volume of Landsat data over specific worldwide land areas, numerous world plots and corresponding statical overviews have been prepared. These presentations include information related to image quality, cloud cover, various types of data overage (i.e. regions, countries, path, rows), acquisition station coverage areas, various archive media formats (i.e. wide band video tapes, computer compatible tapes, high density tapes, etc.) and acquisition time periods (i.e. years, seasons). Plans are to publish this information in a paper sample booklet at the Pecora 12 Symposium, in a USGS circular and on a Landsat CD-ROM; the data will be also be incorporated into GLIS.

A report on the USL NASA/RECON project. Part 2: PC-based R and D in support of IS and R applications

NASA Technical Reports Server (NTRS)

Dominick, Wayne D. (Editor); Chum, Frank Y.; Hall, Philip P.; Moreau, Dennis R.; Triantafyllopoulos, Spiros

1984-01-01

This Working Paper Series entry describes the PC R and D development effort initiated as part of the NASA/RECON Project at the University of Southwestern Louisiana. This effort involves the development of a PC-based environment for the prototyping and evaluation of various tools designed to enhance the interaction between scientists and engineers and remote information systems. The design of PC-based tools for the enhancement of the NASA/RECON university-level courses is described as well as the design of a multi-functional PC-based workstation to support access to and processing of information from local, distributed, and remote sources. Course preparation activities are described in a companion report entitled A Report on the USL NASA/RECON Project: Part 1, the Development of a Transportable, University-Level, IS and R Educational Program, by Suzy Gallagher and Martin Granier, USL/DBMS NASA/RECON Working Paper Series report number DBMS.NASA/RECON-7.

Working Toward a Healthy Planet

NASA Technical Reports Server (NTRS)

Maynard, Nancy G.

2003-01-01

Using information from NASA s Earth Science Public Health Applications Program, Dr. Maynard will address how remote sensing data and associated technologies can be used toward a better understanding of the links among human health, the environment and weather/climate - and, how this increased understanding plus improved information sharing can empower local health and environmental decision-makers to better predict climate-related health problems, take preventive measures, and improve response actions. Remotely-sensed data and observations are providing powerful new tools for addressing climate and environment-related human health problems through increased capabilities for monitoring and surveillance of parameters useful to such problems as infectious and vector-borne diseases, air and water quality, harmful algal blooms, UV radiation, contaminant and pathogen transport in air and water, and thermal stress. NASA s multi-disciplinary scientific team is demonstrating how satellites from their unique vantage point in space can serve as sentinels for weather, climate, and health problems through studies on asthma, malaria, Rift Valley Fever, Asian and African dust, and West Nile Virus

Abstracting of suspected illegal land use in urban areas using case-based classification of remote sensing images

NASA Astrophysics Data System (ADS)

Chen, Fulong; Wang, Chao; Yang, Chengyun; Zhang, Hong; Wu, Fan; Lin, Wenjuan; Zhang, Bo

2008-11-01

This paper proposed a method that uses a case-based classification of remote sensing images and applied this method to abstract the information of suspected illegal land use in urban areas. Because of the discrete cases for imagery classification, the proposed method dealt with the oscillation of spectrum or backscatter within the same land use category, and it not only overcame the deficiency of maximum likelihood classification (the prior probability of land use could not be obtained) but also inherited the advantages of the knowledge-based classification system, such as artificial intelligence and automatic characteristics. Consequently, the proposed method could do the classifying better. Then the researchers used the object-oriented technique for shadow removal in highly dense city zones. With multi-temporal SPOT 5 images whose resolution was 2.5×2.5 meters, the researchers found that the method can abstract suspected illegal land use information in urban areas using post-classification comparison technique.

Design of electrocardiography measurement system with an algorithm to remove noise

NASA Astrophysics Data System (ADS)

Kwon, Hyeokjun; Oh, Sechang; Kumar, Prashanth; Varadan, Vijay K.

2011-04-01

Electrocardiography (ECG) is an important diagnostic tool that can provide vital information about diseases that may not be detectable with other biological signals like, SpO2(Oxygen Saturation), pulse rate, respiration, and blood pressure. For this reason, EKG measurement is mandatory for accurate diagnosis. Recent development in information technology has facilitated remote monitoring systems which can check patient's current status. Moreover, remote monitoring systems can obviate the need for patients to go to hospitals periodically. Such representative wireless communication system is Zigbee sensor network because Zigbee sensor network provides low power consumption and multi-device connection. When we measure EKG signal, another important factor that we should consider is about unexpected signals mixed to EKG signal. The unexpected signals give a severe impact in distorting original EKG signal. There are three kinds of types in noise elements such as muscle noise, movement noise, and respiration noise. This paper describes the design method for EKG measurement system with Zigbee sensor network and proposes an algorithm to remove noises from measured ECG signal.

New method for obtaining position and time structure of source in HDR remote afterloading brachytherapy unit utilizing light emission from scintillator

PubMed Central

Hanada, Takashi; Katsuta, Shoichi; Yorozu, Atsunori; Maruyama, Koichi

2009-01-01

When using a HDR remote afterloading brachytherapy unit, results of treatment can be greatly influenced by both source position and treatment time. The purpose of this study is to obtain information on the source of the HDR remote afterloading unit, such as its position and time structure, with the use of a simple system consisting of a plastic scintillator block and a charge‐coupled device (CCD) camera. The CCD camera was used for recording images of scintillation luminescence at a fixed rate of 30 frames per second in real time. The source position and time structure were obtained by analyzing the recorded images. For a preset source‐step‐interval of 5 mm, the measured value of the source position was 5.0±1.0mm, with a pixel resolution of 0.07 mm in the recorded images. For a preset transit time of 30 s, the measured value was 30.0±0.6 s, when the time resolution of the CCD camera was 1/30 s. This system enabled us to obtain the source dwell time and movement time. Therefore, parameters such as I192r source position, transit time, dwell time, and movement time at each dwell position can be determined quantitatively using this plastic scintillator‐CCD camera system. PACS number: 87.53.Jw

Supporting an emerging workforce: characteristics of rural and remote therapy assistants in Western Australia.

PubMed

Lin, Ivan; Goodale, Belinda; Villanueva, Karen; Spitz, Suzanne

2007-10-01

Multidisciplinary therapy assistants (TAs) are an emerging but poorly understood rural and remote allied health workforce. As an aid to planning and support of TA programs in rural and remote Western Australia (WA), the number, locality and a range of practice variables of rural and remote TAs in WA were determined. Survey questionnaire. Rural and remote regions of WA. Allied health professionals, TAs, TA coordinators and managers of allied health in country regions of WA. Information was gathered on TA location, qualifications, employing organisation, allied health disciplines TAs work with, supervision practices, role and work scenarios. Ninety-eight TAs were identified in rural and remote WA with a further 23 vacant TA positions. Most TAs work across multiple allied health disciplines, half are located at a distance to their supervisors, and very few have a recognised qualification for their TA work. A substantial rural and remote TA workforce was found. A range of TA characteristics were identified that have considerable relevance to the future planning of TA initiatives in rural and remote WA.

Voxel-Based LIDAR Analysis and Applications

NASA Astrophysics Data System (ADS)

Hagstrom, Shea T.

One of the greatest recent changes in the field of remote sensing is the addition of high-quality Light Detection and Ranging (LIDAR) instruments. In particular, the past few decades have been greatly beneficial to these systems because of increases in data collection speed and accuracy, as well as a reduction in the costs of components. These improvements allow modern airborne instruments to resolve sub-meter details, making them ideal for a wide variety of applications. Because LIDAR uses active illumination to capture 3D information, its output is fundamentally different from other modalities. Despite this difference, LIDAR datasets are often processed using methods appropriate for 2D images and that do not take advantage of its primary virtue of 3-dimensional data. It is this problem we explore by using volumetric voxel modeling. Voxel-based analysis has been used in many applications, especially medical imaging, but rarely in traditional remote sensing. In part this is because the memory requirements are substantial when handling large areas, but with modern computing and storage this is no longer a significant impediment. Our reason for using voxels to model scenes from LIDAR data is that there are several advantages over standard triangle-based models, including better handling of overlapping surfaces and complex shapes. We show how incorporating system position information from early in the LIDAR point cloud generation process allows radiometrically-correct transmission and other novel voxel properties to be recovered. This voxelization technique is validated on simulated data using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) software, a first-principles based ray-tracer developed at the Rochester Institute of Technology. Voxel-based modeling of LIDAR can be useful on its own, but we believe its primary advantage is when applied to problems where simpler surface-based 3D models conflict with the requirement of realistic geometry. To show the voxel model's advantage, we apply it to several outstanding problems in remote sensing: LIDAR quality metrics, line-of-sight mapping, and multi-model fusion. Each of these applications is derived, validated, and examined in detail, and our results compared with other state-of-the-art methods. In most cases the voxel-based methods demonstrate superior results and are able to derive information not available to existing methods. Realizing these improvements requires only a shift away from traditional 3D model generation, and our results give a small indicator of what is possible. Many examples of possible areas for future improvement and expansion of algorithms beyond the scope of our work are also noted.

An integrated healthcare enterprise information portal and healthcare information system framework.

PubMed

Hsieh, S L; Lai, Feipei; Cheng, P H; Chen, J L; Lee, H H; Tsai, W N; Weng, Y C; Hsieh, S H; Hsu, K P; Ko, L F; Yang, T H; Chen, C H

2006-01-01

The paper presents an integrated, distributed Healthcare Enterprise Information Portal (HEIP) and Hospital Information Systems (HIS) framework over wireless/wired infrastructure at National Taiwan University Hospital (NTUH). A single sign-on solution for the hospital customer relationship management (CRM) in HEIP has been established. The outcomes of the newly developed Outpatient Information Systems (OIS) in HIS are discussed. The future HEIP blueprints with CRM oriented features: e-Learning, Remote Consultation and Diagnosis (RCD), as well as on-Line Vaccination Services are addressed. Finally, the integrated HEIP and HIS architectures based on the middleware technologies are proposed along with the feasible approaches. The preliminary performance of multi-media, time-based data exchanges over the wireless HEIP side is collected to evaluate the efficiency of the architecture.

Multi-Sensor Registration of Earth Remotely Sensed Imagery

NASA Technical Reports Server (NTRS)

LeMoigne, Jacqueline; Cole-Rhodes, Arlene; Eastman, Roger; Johnson, Kisha; Morisette, Jeffrey; Netanyahu, Nathan S.; Stone, Harold S.; Zavorin, Ilya; Zukor, Dorothy (Technical Monitor)

2001-01-01

Assuming that approximate registration is given within a few pixels by a systematic correction system, we develop automatic image registration methods for multi-sensor data with the goal of achieving sub-pixel accuracy. Automatic image registration is usually defined by three steps; feature extraction, feature matching, and data resampling or fusion. Our previous work focused on image correlation methods based on the use of different features. In this paper, we study different feature matching techniques and present five algorithms where the features are either original gray levels or wavelet-like features, and the feature matching is based on gradient descent optimization, statistical robust matching, and mutual information. These algorithms are tested and compared on several multi-sensor datasets covering one of the EOS Core Sites, the Konza Prairie in Kansas, from four different sensors: IKONOS (4m), Landsat-7/ETM+ (30m), MODIS (500m), and SeaWIFS (1000m).

Registration of Laser Scanning Point Clouds: A Review.

PubMed

Cheng, Liang; Chen, Song; Liu, Xiaoqiang; Xu, Hao; Wu, Yang; Li, Manchun; Chen, Yanming

2018-05-21

The integration of multi-platform, multi-angle, and multi-temporal LiDAR data has become important for geospatial data applications. This paper presents a comprehensive review of LiDAR data registration in the fields of photogrammetry and remote sensing. At present, a coarse-to-fine registration strategy is commonly used for LiDAR point clouds registration. The coarse registration method is first used to achieve a good initial position, based on which registration is then refined utilizing the fine registration method. According to the coarse-to-fine framework, this paper reviews current registration methods and their methodologies, and identifies important differences between them. The lack of standard data and unified evaluation systems is identified as a factor limiting objective comparison of different methods. The paper also describes the most commonly-used point cloud registration error analysis methods. Finally, avenues for future work on LiDAR data registration in terms of applications, data, and technology are discussed. In particular, there is a need to address registration of multi-angle and multi-scale data from various newly available types of LiDAR hardware, which will play an important role in diverse applications such as forest resource surveys, urban energy use, cultural heritage protection, and unmanned vehicles.

Registration of Laser Scanning Point Clouds: A Review

PubMed Central

Cheng, Liang; Chen, Song; Xu, Hao; Wu, Yang; Li, Manchun

2018-01-01

The integration of multi-platform, multi-angle, and multi-temporal LiDAR data has become important for geospatial data applications. This paper presents a comprehensive review of LiDAR data registration in the fields of photogrammetry and remote sensing. At present, a coarse-to-fine registration strategy is commonly used for LiDAR point clouds registration. The coarse registration method is first used to achieve a good initial position, based on which registration is then refined utilizing the fine registration method. According to the coarse-to-fine framework, this paper reviews current registration methods and their methodologies, and identifies important differences between them. The lack of standard data and unified evaluation systems is identified as a factor limiting objective comparison of different methods. The paper also describes the most commonly-used point cloud registration error analysis methods. Finally, avenues for future work on LiDAR data registration in terms of applications, data, and technology are discussed. In particular, there is a need to address registration of multi-angle and multi-scale data from various newly available types of LiDAR hardware, which will play an important role in diverse applications such as forest resource surveys, urban energy use, cultural heritage protection, and unmanned vehicles. PMID:29883397

A Multi-Sensorial Simultaneous Localization and Mapping (SLAM) System for Low-Cost Micro Aerial Vehicles in GPS-Denied Environments

PubMed Central

López, Elena; García, Sergio; Barea, Rafael; Bergasa, Luis M.; Molinos, Eduardo J.; Arroyo, Roberto; Romera, Eduardo; Pardo, Samuel

2017-01-01

One of the main challenges of aerial robots navigation in indoor or GPS-denied environments is position estimation using only the available onboard sensors. This paper presents a Simultaneous Localization and Mapping (SLAM) system that remotely calculates the pose and environment map of different low-cost commercial aerial platforms, whose onboard computing capacity is usually limited. The proposed system adapts to the sensory configuration of the aerial robot, by integrating different state-of-the art SLAM methods based on vision, laser and/or inertial measurements using an Extended Kalman Filter (EKF). To do this, a minimum onboard sensory configuration is supposed, consisting of a monocular camera, an Inertial Measurement Unit (IMU) and an altimeter. It allows to improve the results of well-known monocular visual SLAM methods (LSD-SLAM and ORB-SLAM are tested and compared in this work) by solving scale ambiguity and providing additional information to the EKF. When payload and computational capabilities permit, a 2D laser sensor can be easily incorporated to the SLAM system, obtaining a local 2.5D map and a footprint estimation of the robot position that improves the 6D pose estimation through the EKF. We present some experimental results with two different commercial platforms, and validate the system by applying it to their position control. PMID:28397758

Influence of multi-source and multi-temporal remotely sensed and ancillary data on the accuracy of random forest classification of wetlands in northern Minnesota

USGS Publications Warehouse

Corcoran, Jennifer M.; Knight, Joseph F.; Gallant, Alisa L.

2013-01-01

Wetland mapping at the landscape scale using remotely sensed data requires both affordable data and an efficient accurate classification method. Random forest classification offers several advantages over traditional land cover classification techniques, including a bootstrapping technique to generate robust estimations of outliers in the training data, as well as the capability of measuring classification confidence. Though the random forest classifier can generate complex decision trees with a multitude of input data and still not run a high risk of over fitting, there is a great need to reduce computational and operational costs by including only key input data sets without sacrificing a significant level of accuracy. Our main questions for this study site in Northern Minnesota were: (1) how does classification accuracy and confidence of mapping wetlands compare using different remote sensing platforms and sets of input data; (2) what are the key input variables for accurate differentiation of upland, water, and wetlands, including wetland type; and (3) which datasets and seasonal imagery yield the best accuracy for wetland classification. Our results show the key input variables include terrain (elevation and curvature) and soils descriptors (hydric), along with an assortment of remotely sensed data collected in the spring (satellite visible, near infrared, and thermal bands; satellite normalized vegetation index and Tasseled Cap greenness and wetness; and horizontal-horizontal (HH) and horizontal-vertical (HV) polarization using L-band satellite radar). We undertook this exploratory analysis to inform decisions by natural resource managers charged with monitoring wetland ecosystems and to aid in designing a system for consistent operational mapping of wetlands across landscapes similar to those found in Northern Minnesota.

Operational Tree Species Mapping in a Diverse Tropical Forest with Airborne Imaging Spectroscopy.

PubMed

Baldeck, Claire A; Asner, Gregory P; Martin, Robin E; Anderson, Christopher B; Knapp, David E; Kellner, James R; Wright, S Joseph

2015-01-01

Remote identification and mapping of canopy tree species can contribute valuable information towards our understanding of ecosystem biodiversity and function over large spatial scales. However, the extreme challenges posed by highly diverse, closed-canopy tropical forests have prevented automated remote species mapping of non-flowering tree crowns in these ecosystems. We set out to identify individuals of three focal canopy tree species amongst a diverse background of tree and liana species on Barro Colorado Island, Panama, using airborne imaging spectroscopy data. First, we compared two leading single-class classification methods--binary support vector machine (SVM) and biased SVM--for their performance in identifying pixels of a single focal species. From this comparison we determined that biased SVM was more precise and created a multi-species classification model by combining the three biased SVM models. This model was applied to the imagery to identify pixels belonging to the three focal species and the prediction results were then processed to create a map of focal species crown objects. Crown-level cross-validation of the training data indicated that the multi-species classification model had pixel-level producer's accuracies of 94-97% for the three focal species, and field validation of the predicted crown objects indicated that these had user's accuracies of 94-100%. Our results demonstrate the ability of high spatial and spectral resolution remote sensing to accurately detect non-flowering crowns of focal species within a diverse tropical forest. We attribute the success of our model to recent classification and mapping techniques adapted to species detection in diverse closed-canopy forests, which can pave the way for remote species mapping in a wider variety of ecosystems.

Operational Tree Species Mapping in a Diverse Tropical Forest with Airborne Imaging Spectroscopy

PubMed Central

Baldeck, Claire A.; Asner, Gregory P.; Martin, Robin E.; Anderson, Christopher B.; Knapp, David E.; Kellner, James R.; Wright, S. Joseph

2015-01-01

Remote identification and mapping of canopy tree species can contribute valuable information towards our understanding of ecosystem biodiversity and function over large spatial scales. However, the extreme challenges posed by highly diverse, closed-canopy tropical forests have prevented automated remote species mapping of non-flowering tree crowns in these ecosystems. We set out to identify individuals of three focal canopy tree species amongst a diverse background of tree and liana species on Barro Colorado Island, Panama, using airborne imaging spectroscopy data. First, we compared two leading single-class classification methods—binary support vector machine (SVM) and biased SVM—for their performance in identifying pixels of a single focal species. From this comparison we determined that biased SVM was more precise and created a multi-species classification model by combining the three biased SVM models. This model was applied to the imagery to identify pixels belonging to the three focal species and the prediction results were then processed to create a map of focal species crown objects. Crown-level cross-validation of the training data indicated that the multi-species classification model had pixel-level producer’s accuracies of 94–97% for the three focal species, and field validation of the predicted crown objects indicated that these had user’s accuracies of 94–100%. Our results demonstrate the ability of high spatial and spectral resolution remote sensing to accurately detect non-flowering crowns of focal species within a diverse tropical forest. We attribute the success of our model to recent classification and mapping techniques adapted to species detection in diverse closed-canopy forests, which can pave the way for remote species mapping in a wider variety of ecosystems. PMID:26153693

Sea Ice Remote Sensing Using Surface Reflected GPS Signals

NASA Technical Reports Server (NTRS)

Komjathy, Attila; Maslanik, James; Zavorotny, Valery U.; Axelrad, Penina; Katzberg, Stephen J.

2000-01-01

This paper describes a new research effort to extend the application of Global Positioning System (GPS) signal reflections, received by airborne instruments, to cryospheric remote sensing. Our experimental results indicate that reflected GPS signals have potential to provide information on the presence and condition of sea and freshwater ice as well as the freeze/thaw state of frozen ground. In this paper we show results from aircraft experiments over the ice pack near Barrow, Alaska indicating correlation between forward-scattered GPS returns and RADARSAT backscattered measurements.

Ground-Based Icing Condition Remote Sensing System Definition

NASA Technical Reports Server (NTRS)

Reehorst, Andrew L.; Koenig, George G.

2001-01-01

This report documents the NASA Glenn Research Center activities to assess and down select remote sensing technologies for the purpose of developing a system capable of measuring icing condition hazards aloft. The information generated by such a remote sensing system is intended for use by the entire aviation community, including flight crews. air traffic controllers. airline dispatchers, and aviation weather forecasters. The remote sensing system must be capable of remotely measuring temperature and liquid water content (LWC), and indicating the presence of super-cooled large droplets (SLD). Technologies examined include Profiling Microwave Radiometer, Dual-Band Radar, Multi-Band Radar, Ka-Band Radar. Polarized Ka-Band Radar, and Multiple Field of View (MFOV) Lidar. The assessment of these systems took place primarily during the Mt. Washington Icing Sensors Project (MWISP) in April 1999 and the Alliance Icing Research Study (AIRS) from November 1999 to February 2000. A discussion of the various sensing technologies is included. The result of the assessment is that no one sensing technology can satisfy all of the stated project goals. Therefore a proposed system includes radiometry and Ka-band radar. A multilevel approach is proposed to allow the future selection of the fielded system based upon required capability and available funding. The most basic level system would be the least capable and least expensive. The next level would increase capability and cost, and the highest level would be the most capable and most expensive to field. The Level 1 system would consist of a Profiling Microwave Radiometer. The Level 2 system would add a Ka-Band Radar. The Level 3 system would add polarization to the Ka-Band Radar. All levels of the system would utilize hardware that is already under development by the U.S. Government. However, to meet the needs of the aviation community, all levels of the system will require further development. In addition to the proposed system, it is also recommended that NASA continue to foster the development of Multi-Band Radar and airborne microwave radiometer technologies.

REMOTE SENSING IN OCEANOGRAPHY.

DTIC Science & Technology

remote sensing from satellites. Sensing of oceanographic variables from aircraft began with the photographing of waves and ice. Since then remote measurement of sea surface temperatures and wave heights have become routine. Sensors tested for oceanographic applications include multi-band color cameras, radar scatterometers, infrared spectrometers and scanners, passive microwave radiometers, and radar imagers. Remote sensing has found its greatest application in providing rapid coverage of large oceanographic areas for synoptic and analysis and

Traffic-Light-Preemption Vehicle-Transponder Software Module

NASA Technical Reports Server (NTRS)

Bachelder, Aaron; Foster, Conrad

2005-01-01

A prototype wireless data-communication and control system automatically modifies the switching of traffic lights to give priority to emergency vehicles. The system, which was reported in several NASA Tech Briefs articles at earlier stages of development, includes a transponder on each emergency vehicle, a monitoring and control unit (an intersection controller) at each intersection equipped with traffic lights, and a central monitoring subsystem. An essential component of the system is a software module executed by a microcontroller in each transponder. This module integrates and broadcasts data on the position, velocity, acceleration, and emergency status of the vehicle. The position, velocity, and acceleration data are derived partly from the Global Positioning System, partly from deductive reckoning, and partly from a diagnostic computer aboard the vehicle. The software module also monitors similar broadcasts from other vehicles and from intersection controllers, informs the driver of which intersections it controls, and generates visible and audible alerts to inform the driver of any other emergency vehicles that are close enough to create a potential hazard. The execution of the software module can be monitored remotely and the module can be upgraded remotely and, hence, automatically

A Police and Insurance Joint Management System Based on High Precision BDS/GPS Positioning

PubMed Central

Zuo, Wenwei; Guo, Chi; Liu, Jingnan; Peng, Xuan; Yang, Min

2018-01-01

Car ownership in China reached 194 million vehicles at the end of 2016. The traffic congestion index (TCI) exceeds 2.0 during rush hour in some cities. Inefficient processing for minor traffic accidents is considered to be one of the leading causes for road traffic jams. Meanwhile, the process after an accident is quite troublesome. The main reason is that it is almost always impossible to get the complete chain of evidence when the accident happens. Accordingly, a police and insurance joint management system is developed which is based on high precision BeiDou Navigation Satellite System (BDS)/Global Positioning System (GPS) positioning to process traffic accidents. First of all, an intelligent vehicle rearview mirror terminal is developed. The terminal applies a commonly used consumer electronic device with single frequency navigation. Based on the high precision BDS/GPS positioning algorithm, its accuracy can reach sub-meter level in the urban areas. More specifically, a kernel driver is built to realize the high precision positioning algorithm in an Android HAL layer. Thus the third-party application developers can call the general location Application Programming Interface (API) of the original standard Global Navigation Satellite System (GNSS) to get high precision positioning results. Therefore, the terminal can provide lane level positioning service for car users. Next, a remote traffic accident processing platform is built to provide big data analysis and management. According to the big data analysis of information collected by BDS high precision intelligent sense service, vehicle behaviors can be obtained. The platform can also automatically match and screen the data that uploads after an accident to achieve accurate reproduction of the scene. Thus, it helps traffic police and insurance personnel to complete remote responsibility identification and survey for the accident. Thirdly, a rapid processing flow is established in this article to meet the requirements to quickly handle traffic accidents. The traffic police can remotely identify accident responsibility and the insurance personnel can remotely survey an accident. Moreover, the police and insurance joint management system has been carried out in Wuhan, Central China’s Hubei Province, and Wuxi, Eastern China’s Jiangsu Province. In a word, a system is developed to obtain and analyze multisource data including precise positioning and visual information, and a solution is proposed for efficient processing of traffic accidents. PMID:29320406

A Police and Insurance Joint Management System Based on High Precision BDS/GPS Positioning.

PubMed

Zuo, Wenwei; Guo, Chi; Liu, Jingnan; Peng, Xuan; Yang, Min

2018-01-10

Car ownership in China reached 194 million vehicles at the end of 2016. The traffic congestion index (TCI) exceeds 2.0 during rush hour in some cities. Inefficient processing for minor traffic accidents is considered to be one of the leading causes for road traffic jams. Meanwhile, the process after an accident is quite troublesome. The main reason is that it is almost always impossible to get the complete chain of evidence when the accident happens. Accordingly, a police and insurance joint management system is developed which is based on high precision BeiDou Navigation Satellite System (BDS)/Global Positioning System (GPS) positioning to process traffic accidents. First of all, an intelligent vehicle rearview mirror terminal is developed. The terminal applies a commonly used consumer electronic device with single frequency navigation. Based on the high precision BDS/GPS positioning algorithm, its accuracy can reach sub-meter level in the urban areas. More specifically, a kernel driver is built to realize the high precision positioning algorithm in an Android HAL layer. Thus the third-party application developers can call the general location Application Programming Interface (API) of the original standard Global Navigation Satellite System (GNSS) to get high precision positioning results. Therefore, the terminal can provide lane level positioning service for car users. Next, a remote traffic accident processing platform is built to provide big data analysis and management. According to the big data analysis of information collected by BDS high precision intelligent sense service, vehicle behaviors can be obtained. The platform can also automatically match and screen the data that uploads after an accident to achieve accurate reproduction of the scene. Thus, it helps traffic police and insurance personnel to complete remote responsibility identification and survey for the accident. Thirdly, a rapid processing flow is established in this article to meet the requirements to quickly handle traffic accidents. The traffic police can remotely identify accident responsibility and the insurance personnel can remotely survey an accident. Moreover, the police and insurance joint management system has been carried out in Wuhan, Central China's Hubei Province, and Wuxi, Eastern China's Jiangsu Province. In a word, a system is developed to obtain and analyze multisource data including precise positioning and visual information, and a solution is proposed for efficient processing of traffic accidents.

Landslide inventory maps: New tools for an old problem

NASA Astrophysics Data System (ADS)

Guzzetti, Fausto; Mondini, Alessandro Cesare; Cardinali, Mauro; Fiorucci, Federica; Santangelo, Michele; Chang, Kang-Tsung

2012-04-01

Landslides are present in all continents, and play an important role in the evolution of landscapes. They also represent a serious hazard in many areas of the world. Despite their importance, we estimate that landslide maps cover less than 1% of the slopes in the landmasses, and systematic information on the type, abundance, and distribution of landslides is lacking. Preparing landslide maps is important to document the extent of landslide phenomena in a region, to investigate the distribution, types, pattern, recurrence and statistics of slope failures, to determine landslide susceptibility, hazard, vulnerability and risk, and to study the evolution of landscapes dominated by mass-wasting processes. Conventional methods for the production of landslide maps rely chiefly on the visual interpretation of stereoscopic aerial photography, aided by field surveys. These methods are time consuming and resource intensive. New and emerging techniques based on satellite, airborne, and terrestrial remote sensing technologies, promise to facilitate the production of landslide maps, reducing the time and resources required for their compilation and systematic update. In this work, we first outline the principles for landslide mapping, and we review the conventional methods for the preparation of landslide maps, including geomorphological, event, seasonal, and multi-temporal inventories. Next, we examine recent and new technologies for landslide mapping, considering (i) the exploitation of very-high resolution digital elevation models to analyze surface morphology, (ii) the visual interpretation and semi-automatic analysis of different types of satellite images, including panchromatic, multispectral, and synthetic aperture radar images, and (iii) tools that facilitate landslide field mapping. Next, we discuss the advantages and the limitations of the new remote sensing data and technology for the production of geomorphological, event, seasonal, and multi-temporal inventory maps. We conclude by arguing that the new tools will help to improve the quality of landslide maps, with positive effects on all derivative products and analyses, including erosion studies and landscape modeling, susceptibility and hazard assessments, and risk evaluations.

Future of Land Remote Sensing: What is Needed

NASA Technical Reports Server (NTRS)

Goward, Samuel N.

2007-01-01

A viewgraph presentation describing the future of land remote sensing and the new technologies needed for clear views of the Earth is shown. The contents include: 1) Viewing the Earth; 2) Multi-Imagery; 3) May Missions and Sensors; 4) What is Needed; 5) Things to Think About; 6) Global Land Remote Sensing in Landsat 7 Era; 7) Seasonality; 8) Cloud Contamination; 9) NRC Decadal Study; 10) Atmospheric Attenuation; 11) Geo-Registration; 12) Orthorectification Required; 13) Band Registration with OLI; and 14) Things to Do. A viewgraph presentation describing the future of land remote sensing and the new technologies needed for clear views of the Earth is shown. The contents include: 1) Viewing the Earth; 2) Multi-Imagery; 3) May Missions and Sensors; 4) What is Needed; 5) Things to Think About; 6) Global Land Remote Sensing in Landsat 7 Era; 7) Seasonality; 8) Cloud Contamination; 9) NRC Decadal Study; 10) Atmospheric Attenuation; 11) Geo-Registration; 12) Orthorectification Required; 13) Band Registration with OLI; and 14) Things to Do.

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.

Novel Hyperspectral Sun Photometer for Satellite Remote Sensing Data Radiometric Calibration and Atmospheric Aerosol Studies

NASA Technical Reports Server (NTRS)

Pagnutti, Mary; Ryan, Robert E.; Holekamp, Kara; Harrington, Gary; Frisbie, Troy

2006-01-01

A simple and cost-effective, hyperspectral sun photometer for radiometric vicarious remote sensing system calibration, air quality monitoring, and potentially in-situ planetary climatological studies, was developed. The device was constructed solely from off the shelf components and was designed to be easily deployable for support of short-term verification and validation data collects. This sun photometer not only provides the same data products as existing multi-band sun photometers, this device requires a simpler setup, less data acquisition time and allows for a more direct calibration approach. Fielding this instrument has also enabled Stennis Space Center (SSC) Applied Sciences Directorate personnel to cross calibrate existing sun photometers. This innovative research will position SSC personnel to perform air quality assessments in support of the NASA Applied Sciences Program's National Applications program element as well as to develop techniques to evaluate aerosols in a Martian or other planetary atmosphere.

Remote Sensing Systems to Detect and Analyze Oil Spills on the U.S. Outer Continental Shelf - A State of the Art Assessment

DTIC Science & Technology

2016-08-18

multi- sensor remote sensing approach to describe the distribution of oil from the DWH spill. They used airborne and satellite , multi- and hyperspectral...Experimental Sensors e.g., Acoustic and Nuclear Magnetic Resonance (NMR) (Fingas and Brown, 2012; Puestow et al., 2013). These are further...ship, aerial - aircraft, aerostat or UAV, or satellite ), among other classification criteria. A comprehensive review of sensor categories employed

Cellular phone enabled non-invasive tissue classifier.

PubMed

Laufer, Shlomi; Rubinsky, Boris

2009-01-01

Cellular phone technology is emerging as an important tool in the effort to provide advanced medical care to the majority of the world population currently without access to such care. In this study, we show that non-invasive electrical measurements and the use of classifier software can be combined with cellular phone technology to produce inexpensive tissue characterization. This concept was demonstrated by the use of a Support Vector Machine (SVM) classifier to distinguish through the cellular phone between heart and kidney tissue via the non-invasive multi-frequency electrical measurements acquired around the tissues. After the measurements were performed at a remote site, the raw data were transmitted through the cellular phone to a central computational site and the classifier was applied to the raw data. The results of the tissue analysis were returned to the remote data measurement site. The classifiers correctly determined the tissue type with a specificity of over 90%. When used for the detection of malignant tumors, classifiers can be designed to produce false positives in order to ensure that no tumors will be missed. This mode of operation has applications in remote non-invasive tissue diagnostics in situ in the body, in combination with medical imaging, as well as in remote diagnostics of biopsy samples in vitro.

Simultaneously precise frequency transfer and time synchronization using feed-forward compensation technique via 120 km fiber link.

PubMed

Chen, Xing; Lu, Jinlong; Cui, Yifan; Zhang, Jian; Lu, Xing; Tian, Xusheng; Ci, Cheng; Liu, Bo; Wu, Hong; Tang, Tingsong; Shi, Kebin; Zhang, Zhigang

2015-12-22

Precision time synchronization between two remote sites is desired in many applications such as global positioning satellite systems, long-baseline interferometry, coherent radar detection and fundamental physics constant measurements. The recently developed frequency dissemination technologies based on optical fiber link have improved the transfer instability to the level of 10(-19)/day at remote location. Therefore it is possible to keep clock oscillation at remote locations continuously corrected, or to reproduce a "virtual" clock on the remote location. However the initial alignment and the correction of 1 pps timing signal from time to time are still required, besides the highly stabilized clock frequency transfer between distant locations. Here we demonstrate a time synchronization based on an ultra-stable frequency transfer system via 120-km commercial fiber link by transferring an optical frequency comb. Both the phase noise compensation in frequency dissemination and temporal basis alignment in time synchronization were implemented by a feed-forward digital compensation (FFDC) technique. The fractional frequency instability was measured to be 6.18 × 10(-20) at 2000 s. The timing deviation of time synchronization was measured to be 0.6 ps in 1500 s. This technique also can be applied in multi-node fiber network topology.

Simultaneously precise frequency transfer and time synchronization using feed-forward compensation technique via 120 km fiber link

PubMed Central

Chen, Xing; Lu, Jinlong; Cui, Yifan; Zhang, Jian; Lu, Xing; Tian, Xusheng; Ci, Cheng; Liu, Bo; Wu, Hong; Tang, Tingsong; Shi, Kebin; Zhang, Zhigang

2015-01-01

Precision time synchronization between two remote sites is desired in many applications such as global positioning satellite systems, long-baseline interferometry, coherent radar detection and fundamental physics constant measurements. The recently developed frequency dissemination technologies based on optical fiber link have improved the transfer instability to the level of 10−19/day at remote location. Therefore it is possible to keep clock oscillation at remote locations continuously corrected, or to reproduce a “virtual” clock on the remote location. However the initial alignment and the correction of 1 pps timing signal from time to time are still required, besides the highly stabilized clock frequency transfer between distant locations. Here we demonstrate a time synchronization based on an ultra-stable frequency transfer system via 120-km commercial fiber link by transferring an optical frequency comb. Both the phase noise compensation in frequency dissemination and temporal basis alignment in time synchronization were implemented by a feed-forward digital compensation (FFDC) technique. The fractional frequency instability was measured to be 6.18 × 10−20 at 2000 s. The timing deviation of time synchronization was measured to be 0.6 ps in 1500 s. This technique also can be applied in multi-node fiber network topology. PMID:26691731

Cellular Phone Enabled Non-Invasive Tissue Classifier

PubMed Central

Laufer, Shlomi; Rubinsky, Boris

2009-01-01

Cellular phone technology is emerging as an important tool in the effort to provide advanced medical care to the majority of the world population currently without access to such care. In this study, we show that non-invasive electrical measurements and the use of classifier software can be combined with cellular phone technology to produce inexpensive tissue characterization. This concept was demonstrated by the use of a Support Vector Machine (SVM) classifier to distinguish through the cellular phone between heart and kidney tissue via the non-invasive multi-frequency electrical measurements acquired around the tissues. After the measurements were performed at a remote site, the raw data were transmitted through the cellular phone to a central computational site and the classifier was applied to the raw data. The results of the tissue analysis were returned to the remote data measurement site. The classifiers correctly determined the tissue type with a specificity of over 90%. When used for the detection of malignant tumors, classifiers can be designed to produce false positives in order to ensure that no tumors will be missed. This mode of operation has applications in remote non-invasive tissue diagnostics in situ in the body, in combination with medical imaging, as well as in remote diagnostics of biopsy samples in vitro. PMID:19365554

Research Status and Development Trend of Remote Sensing in China Using Bibliometric Analysis

NASA Astrophysics Data System (ADS)

Zeng, Y.; Zhang, J.; Niu, R.

2015-06-01

Remote sensing was introduced into China in 1970s and then began to flourish. At present, China has developed into a big remote sensing country, and remote sensing is increasingly playing an important role in various fields of national economic construction and social development. Based on China Academic Journals Full-text Database and China Citation Database published by China National Knowledge Infrastructure, this paper analyzed academic characteristics of 963 highly cited papers published by 16 professional and academic journals in the field of surveying and mapping from January 2010 to December 2014 in China, which include hot topics, literature authors, research institutions, and fundations. At the same time, it studied a total of 51,149 keywords published by these 16 journals during the same period. Firstly by keyword selection, keyword normalization, keyword consistency and keyword incorporation, and then by analysis of high frequency keywords, the progress and prospect of China's remote sensing technology in data acquisition, data processing and applications during the past five years were further explored and revealed. It can be seen that: highly cited paper analysis and word frequency analysis is complementary on subject progress analysis; in data acquisition phase, research focus is new civilian remote sensing satellite systems and UAV remote sensing system; research focus of data processing and analysis is multi-source information extraction and classification, laser point cloud data processing, objectoriented high resolution image analysis, SAR data and hyper-spectral image processing, etc.; development trend of remote sensing data processing is quantitative, intelligent, automated, and real-time, and the breadth and depth of remote sensing application is gradually increased; parallel computing, cloud computing and geographic conditions monitoring and census are the new research focuses to be paid attention to.

Improving PERSIANN-CCS rain estimation using probabilistic approach and multi-sensors information

NASA Astrophysics Data System (ADS)

Karbalaee, N.; Hsu, K. L.; Sorooshian, S.; Kirstetter, P.; Hong, Y.

2016-12-01

This presentation discusses the recent implemented approaches to improve the rainfall estimation from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Cloud Classification System (PERSIANN-CCS). PERSIANN-CCS is an infrared (IR) based algorithm being integrated in the IMERG (Integrated Multi-Satellite Retrievals for the Global Precipitation Mission GPM) to create a precipitation product in 0.1x0.1degree resolution over the chosen domain 50N to 50S every 30 minutes. Although PERSIANN-CCS has a high spatial and temporal resolution, it overestimates or underestimates due to some limitations.PERSIANN-CCS can estimate rainfall based on the extracted information from IR channels at three different temperature threshold levels (220, 235, and 253k). This algorithm relies only on infrared data to estimate rainfall indirectly from this channel which cause missing the rainfall from warm clouds and false estimation for no precipitating cold clouds. In this research the effectiveness of using other channels of GOES satellites such as visible and water vapors has been investigated. By using multi-sensors the precipitation can be estimated based on the extracted information from multiple channels. Also, instead of using the exponential function for estimating rainfall from cloud top temperature, the probabilistic method has been used. Using probability distributions of precipitation rates instead of deterministic values has improved the rainfall estimation for different type of clouds.

Buildings Change Detection Based on Shape Matching for Multi-Resolution Remote Sensing Imagery

NASA Astrophysics Data System (ADS)

Abdessetar, M.; Zhong, Y.

2017-09-01

Buildings change detection has the ability to quantify the temporal effect, on urban area, for urban evolution study or damage assessment in disaster cases. In this context, changes analysis might involve the utilization of the available satellite images with different resolutions for quick responses. In this paper, to avoid using traditional method with image resampling outcomes and salt-pepper effect, building change detection based on shape matching is proposed for multi-resolution remote sensing images. Since the object's shape can be extracted from remote sensing imagery and the shapes of corresponding objects in multi-scale images are similar, it is practical for detecting buildings changes in multi-scale imagery using shape analysis. Therefore, the proposed methodology can deal with different pixel size for identifying new and demolished buildings in urban area using geometric properties of objects of interest. After rectifying the desired multi-dates and multi-resolutions images, by image to image registration with optimal RMS value, objects based image classification is performed to extract buildings shape from the images. Next, Centroid-Coincident Matching is conducted, on the extracted building shapes, based on the Euclidean distance measurement between shapes centroid (from shape T0 to shape T1 and vice versa), in order to define corresponding building objects. Then, New and Demolished buildings are identified based on the obtained distances those are greater than RMS value (No match in the same location).

Integrated web-based viewing and secure remote access to a clinical data repository and diverse clinical systems.

PubMed

Duncan, R G; Saperia, D; Dulbandzhyan, R; Shabot, M M; Polaschek, J X; Jones, D T

2001-01-01

The advent of the World-Wide-Web protocols and client-server technology has made it easy to build low-cost, user-friendly, platform-independent graphical user interfaces to health information systems and to integrate the presentation of data from multiple systems. The authors describe a Web interface for a clinical data repository (CDR) that was moved from concept to production status in less than six months using a rapid prototyping approach, multi-disciplinary development team, and off-the-shelf hardware and software. The system has since been expanded to provide an integrated display of clinical data from nearly 20 disparate information systems.

Exploring diversity in ensemble classification: Applications in large area land cover mapping

NASA Astrophysics Data System (ADS)

Mellor, Andrew; Boukir, Samia

2017-07-01

Ensemble classifiers, such as random forests, are now commonly applied in the field of remote sensing, and have been shown to perform better than single classifier systems, resulting in reduced generalisation error. Diversity across the members of ensemble classifiers is known to have a strong influence on classification performance - whereby classifier errors are uncorrelated and more uniformly distributed across ensemble members. The relationship between ensemble diversity and classification performance has not yet been fully explored in the fields of information science and machine learning and has never been examined in the field of remote sensing. This study is a novel exploration of ensemble diversity and its link to classification performance, applied to a multi-class canopy cover classification problem using random forests and multisource remote sensing and ancillary GIS data, across seven million hectares of diverse dry-sclerophyll dominated public forests in Victoria Australia. A particular emphasis is placed on analysing the relationship between ensemble diversity and ensemble margin - two key concepts in ensemble learning. The main novelty of our work is on boosting diversity by emphasizing the contribution of lower margin instances used in the learning process. Exploring the influence of tree pruning on diversity is also a new empirical analysis that contributes to a better understanding of ensemble performance. Results reveal insights into the trade-off between ensemble classification accuracy and diversity, and through the ensemble margin, demonstrate how inducing diversity by targeting lower margin training samples is a means of achieving better classifier performance for more difficult or rarer classes and reducing information redundancy in classification problems. Our findings inform strategies for collecting training data and designing and parameterising ensemble classifiers, such as random forests. This is particularly important in large area remote sensing applications, for which training data is costly and resource intensive to collect.

Algorithm and Application of Gcp-Independent Block Adjustment for Super Large-Scale Domestic High Resolution Optical Satellite Imagery

NASA Astrophysics Data System (ADS)

Sun, Y. S.; Zhang, L.; Xu, B.; Zhang, Y.

2018-04-01

The accurate positioning of optical satellite image without control is the precondition for remote sensing application and small/medium scale mapping in large abroad areas or with large-scale images. In this paper, aiming at the geometric features of optical satellite image, based on a widely used optimization method of constraint problem which is called Alternating Direction Method of Multipliers (ADMM) and RFM least-squares block adjustment, we propose a GCP independent block adjustment method for the large-scale domestic high resolution optical satellite image - GISIBA (GCP-Independent Satellite Imagery Block Adjustment), which is easy to parallelize and highly efficient. In this method, the virtual "average" control points are built to solve the rank defect problem and qualitative and quantitative analysis in block adjustment without control. The test results prove that the horizontal and vertical accuracy of multi-covered and multi-temporal satellite images are better than 10 m and 6 m. Meanwhile the mosaic problem of the adjacent areas in large area DOM production can be solved if the public geographic information data is introduced as horizontal and vertical constraints in the block adjustment process. Finally, through the experiments by using GF-1 and ZY-3 satellite images over several typical test areas, the reliability, accuracy and performance of our developed procedure will be presented and studied in this paper.

Optical assessment of nonimaging concentrators.

PubMed

Timinger, A; Kribus, A; Ries, H; Smith, T; Walther, M

2000-11-01

An optical measurement method for nonimaging radiation concentrators is proposed. A Lambertian light source is placed in the exit aperture of the concentrator. Looking into the concentrator's entrance aperture from a remote position, one can photograph the transmission patterns. The patterns show the transmission of radiation through the concentrator with the full resolution of the four-dimensional phase space of geometric optics. By matching ray-tracing simulations to the measurement, one can achieve detailed and accurate information about the geometry of the concentrator. This is a remote, noncontact measurement and can be performed in situ for installed concentrators. Additional information regarding small-scale reflector waviness and surface reflectivity can also be obtained from the same measurement with additional analysis.

Geometric aspects in digital analysis of Multi-Spectral Scanner (MSS) data

NASA Technical Reports Server (NTRS)

Mikhail, E. M.; Baker, J. R.

1973-01-01

Present automated systems of interpretation which apply pattern recognition techniques on MSS data do not fully consider the geometry of the acquisition system. In an effort to improve the usefulness of the MSS data when digitally treated, geometric aspects are analyzed and discussed. Attempts to correct for scanner instabilities in position and orientation by affine and polynomial transformations, as well as by modified collinearity equations are described. Methods of accounting for panoramic and relief effects are also discussed. It is anticipated that reliable area as well as position determinations can be accomplished during the process of automatic interpretation. A concept for a unified approach to the treatment of remote sensing data, both metric and nonmetric is presented.

Automatic Ship Detection in Remote Sensing Images from Google Earth of Complex Scenes Based on Multiscale Rotation Dense Feature Pyramid Networks

NASA Astrophysics Data System (ADS)

Yang, Xue; Sun, Hao; Fu, Kun; Yang, Jirui; Sun, Xian; Yan, Menglong; Guo, Zhi

2018-01-01

Ship detection has been playing a significant role in the field of remote sensing for a long time but it is still full of challenges. The main limitations of traditional ship detection methods usually lie in the complexity of application scenarios, the difficulty of intensive object detection and the redundancy of detection region. In order to solve such problems above, we propose a framework called Rotation Dense Feature Pyramid Networks (R-DFPN) which can effectively detect ship in different scenes including ocean and port. Specifically, we put forward the Dense Feature Pyramid Network (DFPN), which is aimed at solving the problem resulted from the narrow width of the ship. Compared with previous multi-scale detectors such as Feature Pyramid Network (FPN), DFPN builds the high-level semantic feature-maps for all scales by means of dense connections, through which enhances the feature propagation and encourages the feature reuse. Additionally, in the case of ship rotation and dense arrangement, we design a rotation anchor strategy to predict the minimum circumscribed rectangle of the object so as to reduce the redundant detection region and improve the recall. Furthermore, we also propose multi-scale ROI Align for the purpose of maintaining the completeness of semantic and spatial information. Experiments based on remote sensing images from Google Earth for ship detection show that our detection method based on R-DFPN representation has a state-of-the-art performance.

Domain wall remote pinning in magnetic nano wires

NASA Astrophysics Data System (ADS)

Read, Dan; Miguel, Jorge; Maccherozzi, Francesco; Cavill, Stuart; Dhesi, Sarnjeet; Cardiff University Collaboration; Diamond Light Source Collaboration

2013-03-01

In the current race for information storage media with ever increasing density the position of magnetic domain walls, the region in a magnetic system where the local magnetization continually rotates its direction between adjacent magnetic domains, is one of the most promising routes for future storage media devices. Information storage requires ultrafast read-out and writing operations, but domain walls need to be pinned so that the information is safely stored in the long term. Here we investigate the use of remote magnetostatic charges to trap domain walls. By using X-ray photoelectron emission microscopy we have followed the position of domain walls of opposite charge being pinned or repelled by pinning potentials of increasing strength. Micromagnetic simulations show an excellent agreement with the experimental results. We demonstrate the attractive or repulsive character of the interaction between domain wall and trap depending upon the sign of their magnetic charges. These quasi-static experiments are the antecedent to ultrafast time-resolved XMCD-PEEM experiments where the spin-transfer torque effect will be studied dynamically by applying picosecond-long current pulses across the magnetic nanowire.

Multi-scales and multi-satellites estimates of evapotranspiration with a residual energy balance model in the Muzza agricultural district in Northern Italy

NASA Astrophysics Data System (ADS)

Corbari, C.; Bissolati, M.; Mancini, M.

2015-05-01

Evapotranspiration estimates were performed with a residual energy balance model (REB) over an agricultural area using remote sensing data. REB uses land surface temperature (LST) as main input parameter so that energy fluxes were computed instantaneously at the time of data acquisition. Data from MODIS and SEVIRI sensors were used and downscaling techniques were implemented to improve their spatial resolutions. Energy fluxes at the original spatial resolutions (1000 m for MODIS and 5000 m for SEVIRI) as well as at the downscaled resolutions (250 m for MODIS and 1000 m for SEVIRI) were calculated with the REB model. Ground eddy covariance data and remote sensing information from the Muzza agricultural irrigation district in Italy from 2010 to 2012 gave the opportunity to validate and compare spatially distributed energy fluxes. The model outputs matched quite well ground observations when ground LST data were used, while differences increased when MODIS and SEVIRI LST were used. The spatial analysis revealed significant differences between the two sensors both in term of LST (around 2.8 °C) and of latent heat fluxes with values (around 100 W m-2).

Remote sensing of deep hermatypic coral reefs in Puerto Rico and the U.S. Virgin Islands using the Seabed autonomous underwater vehicle

NASA Astrophysics Data System (ADS)

Armstrong, Roy A.; Singh, Hanumant

2006-09-01

Optical imaging of coral reefs and other benthic communities present below one attenuation depth, the limit of effective airborne and satellite remote sensing, requires the use of in situ platforms such as autonomous underwater vehicles (AUVs). The Seabed AUV, which was designed for high-resolution underwater optical and acoustic imaging, was used to characterize several deep insular shelf reefs of Puerto Rico and the US Virgin Islands using digital imagery. The digital photo transects obtained by the Seabed AUV provided quantitative data on living coral, sponge, gorgonian, and macroalgal cover as well as coral species richness and diversity. Rugosity, an index of structural complexity, was derived from the pencil-beam acoustic data. The AUV benthic assessments could provide the required information for selecting unique areas of high coral cover, biodiversity and structural complexity for habitat protection and ecosystem-based management. Data from Seabed sensors and related imaging technologies are being used to conduct multi-beam sonar surveys, 3-D image reconstruction from a single camera, photo mosaicking, image based navigation, and multi-sensor fusion of acoustic and optical data.

Dynamic information routing in complex networks

PubMed Central

Kirst, Christoph; Timme, Marc; Battaglia, Demian

2016-01-01

Flexible information routing fundamentally underlies the function of many biological and artificial networks. Yet, how such systems may specifically communicate and dynamically route information is not well understood. Here we identify a generic mechanism to route information on top of collective dynamical reference states in complex networks. Switching between collective dynamics induces flexible reorganization of information sharing and routing patterns, as quantified by delayed mutual information and transfer entropy measures between activities of a network's units. We demonstrate the power of this mechanism specifically for oscillatory dynamics and analyse how individual unit properties, the network topology and external inputs co-act to systematically organize information routing. For multi-scale, modular architectures, we resolve routing patterns at all levels. Interestingly, local interventions within one sub-network may remotely determine nonlocal network-wide communication. These results help understanding and designing information routing patterns across systems where collective dynamics co-occurs with a communication function. PMID:27067257

Design and Implementation study of Remote Home Rehabilitation Training Operating System based on Internet

NASA Astrophysics Data System (ADS)

Zhuo, Jin; Chung Gun, Jang

2018-03-01

The proportion of rehabilitation doctors and patients mismatch is very grim in the context of social aging. The Family Rehabilitation System captures the profound information of the trainer’s movements through the kinect bone tracing technique, allowing the doctor to remotely master the patient’s training progress. With the help of computers and the Internet, the patient can consult a physician, while the physician can remotely guide and launch the training “prescription” through the Internet according to the training effect. Patients can have rehabilitated training at home. The results of the test showed that the system has a positive effect on the rehabilitation of the patient.

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...

Remote Sensing Data Visualization, Fusion and Analysis via Giovanni

NASA Technical Reports Server (NTRS)

Leptoukh, G.; Zubko, V.; Gopalan, A.; Khayat, M.

2007-01-01

We describe Giovanni, the NASA Goddard developed online visualization and analysis tool that allows users explore various phenomena without learning remote sensing data formats and downloading voluminous data. Using MODIS aerosol data as an example, we formulate an approach to the data fusion for Giovanni to further enrich online multi-sensor remote sensing data comparison and analysis.

Urban Natural Environments, Obesity, and Health-Related Quality of Life among Hispanic Children Living in Inner-City Neighborhoods

PubMed Central

Kim, Jun-Hyun; Lee, Chanam; Sohn, Wonmin

2016-01-01

Although a substantial body of literature has provided evidence supporting the positive effects of natural environments on well-being, little has been known about the specific spatial patterns of urban nature in promoting health-related quality of life (HRQOL) among children. This study assessed the association that the urban natural environment measured by landscape spatial patterns may have with obesity and HRQOL among Hispanic children. Ninety-two 4th and 5th grade students were recruited from Houston, Texas, and the Pediatric Quality of Life Inventory (PedsQL) was used to capture the children’s HRQOL. The quality of urban natural environments was assessed by quantifying the landscape spatial patterns, using landscape indices generated by Geographic Information Systems and remote sensing. From the bivariate analyses, children’s body mass index showed a significantly negative association with their HRQOL. After controlling for socio-demographic factors, the results revealed that larger and more tree areas were positively correlated with children’s HRQOL. In addition, those children living in areas with tree patches further apart from each other showed higher HRQOL. This research adds to the current multi-disciplinary area of research on environment-health relationships by investigating the roles of urban greeneries and linking their spatial structures with children’s HRQOL. PMID:26771623

Urban Natural Environments, Obesity, and Health-Related Quality of Life among Hispanic Children Living in Inner-City Neighborhoods.

PubMed

Kim, Jun-Hyun; Lee, Chanam; Sohn, Wonmin

2016-01-12

Although a substantial body of literature has provided evidence supporting the positive effects of natural environments on well-being, little has been known about the specific spatial patterns of urban nature in promoting health-related quality of life (HRQOL) among children. This study assessed the association that the urban natural environment measured by landscape spatial patterns may have with obesity and HRQOL among Hispanic children. Ninety-two 4th and 5th grade students were recruited from Houston, Texas, and the Pediatric Quality of Life Inventory (PedsQL) was used to capture the children's HRQOL. The quality of urban natural environments was assessed by quantifying the landscape spatial patterns, using landscape indices generated by Geographic Information Systems and remote sensing. From the bivariate analyses, children's body mass index showed a significantly negative association with their HRQOL. After controlling for socio-demographic factors, the results revealed that larger and more tree areas were positively correlated with children's HRQOL. In addition, those children living in areas with tree patches further apart from each other showed higher HRQOL. This research adds to the current multi-disciplinary area of research on environment-health relationships by investigating the roles of urban greeneries and linking their spatial structures with children's HRQOL.

Photogrammetry and remote sensing for visualization of spatial data in a virtual reality environment

NASA Astrophysics Data System (ADS)

Bhagawati, Dwipen

2001-07-01

Researchers in many disciplines have started using the tool of Virtual Reality (VR) to gain new insights into problems in their respective disciplines. Recent advances in computer graphics, software and hardware technologies have created many opportunities for VR systems, advanced scientific and engineering applications being among them. In Geometronics, generally photogrammetry and remote sensing are used for management of spatial data inventory. VR technology can be suitably used for management of spatial data inventory. This research demonstrates usefulness of VR technology for inventory management by taking the roadside features as a case study. Management of roadside feature inventory involves positioning and visualization of the features. This research has developed a methodology to demonstrate how photogrammetric principles can be used to position the features using the video-logging images and GPS camera positioning and how image analysis can help produce appropriate texture for building the VR, which then can be visualized in a Cave Augmented Virtual Environment (CAVE). VR modeling was implemented in two stages to demonstrate the different approaches for modeling the VR scene. A simulated highway scene was implemented with the brute force approach, while modeling software was used to model the real world scene using feature positions produced in this research. The first approach demonstrates an implementation of the scene by writing C++ codes to include a multi-level wand menu for interaction with the scene that enables the user to interact with the scene. The interactions include editing the features inside the CAVE display, navigating inside the scene, and performing limited geographic analysis. The second approach demonstrates creation of a VR scene for a real roadway environment using feature positions determined in this research. The scene looks realistic with textures from the real site mapped on to the geometry of the scene. Remote sensing and digital image processing techniques were used for texturing the roadway features in this scene.

Development of low-altitude remote sensing systems for crop production management

USDA-ARS?s Scientific Manuscript database

Precision agriculture accounts for within-field variability for targeted treatment rather than uniform treatment of an entire field. Precision agriculture is built on agricultural mechanization and state-of-the-art technologies of geographical information systems (GIS), global positioning systems (G...

Probabilistic Downscaling of Remote Sensing Data with Applications for Multi-Scale Biogeochemical Flux Modeling.

PubMed

Stoy, Paul C; Quaife, Tristan

2015-01-01

Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes.

Probabilistic Downscaling of Remote Sensing Data with Applications for Multi-Scale Biogeochemical Flux Modeling

PubMed Central

Stoy, Paul C.; Quaife, Tristan

2015-01-01

Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes. PMID:26067835

Exploring the Potential of High Resolution Remote Sensing Data for Mapping Vegetation and the Age Groups of Oil Palm Plantation

NASA Astrophysics Data System (ADS)

Kamiran, N.; Sarker, M. L. R.

2014-02-01

The land use/land cover transformation in Malaysia is enormous due to palm oil plantation which has provided huge economical benefits but also created a huge concern for carbon emission and biodiversity. Accurate information about oil palm plantation and the age of plantation is important for a sustainable production, estimation of carbon storage capacity, biodiversity and the climate model. However, the problem is that this information cannot be extracted easily due to the spectral signature for forest and age group of palm oil plantations is similar. Therefore, a noble approach "multi-scale and multi-texture algorithms" was used for mapping vegetation and different age groups of palm oil plantation using a high resolution panchromatic image (WorldView-1) considering the fact that pan imagery has a potential for more detailed and accurate mapping with an effective image processing technique. Seven texture algorithms of second-order Grey Level Co-occurrence Matrix (GLCM) with different scales (from 3×3 to 39×39) were used for texture generation. All texture parameters were classified step by step using a robust classifier "Artificial Neural Network (ANN)". Results indicate that single spectral band was unable to provide good result (overall accuracy = 34.92%), while higher overall classification accuracies (73.48%, 84.76% and 93.18%) were obtained when textural information from multi-scale and multi-texture approach were used in the classification algorithm.

Measuring short-term post-fire forest recovery across a burn severity gradient in a mixed pine-oak forest using multi-sensor remote sensing techniques

DOE PAGES

Meng, Ran; Wu, Jin; Zhao, Feng; ...

2018-06-01

Understanding post-fire forest recovery is pivotal to the study of forest dynamics and global carbon cycle. Field-based studies indicated a convex response of forest recovery rate to burn severity at the individual tree level, related with fire-induced tree mortality; however, these findings were constrained in spatial/temporal extents, while not detectable by traditional optical remote sensing studies, largely attributing to the contaminated effect from understory recovery. For this work, we examined whether the combined use of multi-sensor remote sensing techniques (i.e., 1m simultaneous airborne imaging spectroscopy and LiDAR and 2m satellite multi-spectral imagery) to separate canopy recovery from understory recovery wouldmore » enable to quantify post-fire forest recovery rate spanning a large gradient in burn severity over large-scales. Our study was conducted in a mixed pine-oak forest in Long Island, NY, three years after a top-killing fire. Our studies remotely detected an initial increase and then decline of forest recovery rate to burn severity across the burned area, with a maximum canopy area-based recovery rate of 10% per year at moderate forest burn severity class. More intriguingly, such remotely detected convex relationships also held at species level, with pine trees being more resilient to high burn severity and having a higher maximum recovery rate (12% per year) than oak trees (4% per year). These results are one of the first quantitative evidences showing the effects of fire adaptive strategies on post-fire forest recovery, derived from relatively large spatial-temporal domains. Our study thus provides the methodological advance to link multi-sensor remote sensing techniques to monitor forest dynamics in a spatially explicit manner over large-scales, with important implications for fire-related forest management, and for constraining/benchmarking fire effect schemes in ecological process models.« less

Measuring short-term post-fire forest recovery across a burn severity gradient in a mixed pine-oak forest using multi-sensor remote sensing techniques

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

Meng, Ran; Wu, Jin; Zhao, Feng

Understanding post-fire forest recovery is pivotal to the study of forest dynamics and global carbon cycle. Field-based studies indicated a convex response of forest recovery rate to burn severity at the individual tree level, related with fire-induced tree mortality; however, these findings were constrained in spatial/temporal extents, while not detectable by traditional optical remote sensing studies, largely attributing to the contaminated effect from understory recovery. For this work, we examined whether the combined use of multi-sensor remote sensing techniques (i.e., 1m simultaneous airborne imaging spectroscopy and LiDAR and 2m satellite multi-spectral imagery) to separate canopy recovery from understory recovery wouldmore » enable to quantify post-fire forest recovery rate spanning a large gradient in burn severity over large-scales. Our study was conducted in a mixed pine-oak forest in Long Island, NY, three years after a top-killing fire. Our studies remotely detected an initial increase and then decline of forest recovery rate to burn severity across the burned area, with a maximum canopy area-based recovery rate of 10% per year at moderate forest burn severity class. More intriguingly, such remotely detected convex relationships also held at species level, with pine trees being more resilient to high burn severity and having a higher maximum recovery rate (12% per year) than oak trees (4% per year). These results are one of the first quantitative evidences showing the effects of fire adaptive strategies on post-fire forest recovery, derived from relatively large spatial-temporal domains. Our study thus provides the methodological advance to link multi-sensor remote sensing techniques to monitor forest dynamics in a spatially explicit manner over large-scales, with important implications for fire-related forest management, and for constraining/benchmarking fire effect schemes in ecological process models.« less

Implementation of a national, nurse-led telephone health service in Scotland: assessing the consequences for remote and rural localities.

PubMed

Roberts, A; Heaney, D; Haddow, G; O'Donnell, C A

2009-01-01

Internationally, nurse-led models of telephone triage have become commonplace in unscheduled healthcare delivery. Various existing models have had a positive impact on the delivery of healthcare services, often reducing the demand on accident and emergency departments and staff workload 'out of hours'. Our objective was to assess whether a model of centralised nurse telephone triage (NHS 24, introduced in Scotland in 2001) was appropriate for remote and rural areas. In this qualitative study the views and perspectives of health professionals across Scotland are explored. Thirty-five participants were purposively selected for interviews during 2005. Two types of interview were conducted: detailed, semi-structured, face-to-face interviews with key stakeholders of NHS 24; and briefer telephone interviews with partners from NHS Boards across Scotland. A constant comparative approach was taken to analysis. Ethical approval for the study was obtained from the Scottish Multi-site Research Ethics Committee. The findings are comparable with other research studies of new service developments in remote and rural health care. The rigidity of the centralised triage model introduced, the need to understand variation of health service delivery, and the importance of utilising local professional knowledge were all key issues affecting performance. Remote and rural complexities need to be considered when designing new healthcare services. It is suggested that new health service designs are 'proofed' for remote and rural complexities. This study highlights that a centralised nurse-led telephone triage model was inappropriate for remote and rural Scotland, and may not be appropriate for all geographies and circumstances.

Wireless Sensor Node for Autonomous Monitoring and Alerts in Remote Environments

NASA Technical Reports Server (NTRS)

Panangadan, Anand V. (Inventor); Monacos, Steve P. (Inventor)

2015-01-01

A method, apparatus, system, and computer program products provides personal alert and tracking capabilities using one or more nodes. Each node includes radio transceiver chips operating at different frequency ranges, a power amplifier, sensors, a display, and embedded software. The chips enable the node to operate as either a mobile sensor node or a relay base station node while providing a long distance relay link between nodes. The power amplifier enables a line-of-sight communication between the one or more nodes. The sensors provide a GPS signal, temperature, and accelerometer information (used to trigger an alert condition). The embedded software captures and processes the sensor information, provides a multi-hop packet routing protocol to relay the sensor information to and receive alert information from a command center, and to display the alert information on the display.

Extraction of reduced alteration information based on Aster data: a case study of the Bashibulake uranium ore district

NASA Astrophysics Data System (ADS)

Ye, Fa-wang; Liu, De-chang

2008-12-01

Practices of sandstone-type uranium exploration in recent years in China indicate that the uranium mineralization alteration information is of great importance for selecting a new uranium target or prospecting in outer area of the known uranium ore district. Taking a case study of BASHIBULAKE uranium ore district, this paper mainly presents the technical minds and methods of extracting the reduced alteration information by oil and gas in BASHIBULAKE ore district using ASTER data. First, the regional geological setting and study status in BASHIBULAKE uranium ore district are introduced in brief. Then, the spectral characteristics of altered sandstone and un-altered sandstone in BASHIBULAKE ore district are analyzed deeply. Based on the spectral analysis, two technical minds to extract the remote sensing reduced alteration information are proposed, and the un-mixing method is introduced to process ASTER data to extract the reduced alteration information in BASHIBULAKE ore district. From the enhanced images, three remote sensing anomaly zones are discovered, and their geological and prospecting significances are further made sure by taking the advantages of multi-bands in SWIR of ASTER data. Finally, the distribution and intensity of the reduced alteration information in Cretaceous system and its relationship with the genesis of uranium deposit are discussed, the specific suggestions for uranium prospecting orientation in outer of BASHIBULAKE ore district are also proposed.

Patterns of Sensitivity to Parenting and Peer Environments: Early Temperament and Adolescent Externalizing Behavior.

PubMed

Tung, Irene; Noroña, Amanda N; Morgan, Julia E; Caplan, Barbara; Lee, Steve S; Baker, Bruce L

2018-03-14

Although parenting behavior and friendship quality predict adolescent externalizing behaviors (EBs), individual differences in temperament may differentially affect susceptibility to these factors over time. In a multi-method and multi-informant study of 141 children followed prospectively from toddlerhood to adolescence, we tested the independent and interactive associations of age 3 reactive temperament (e.g., negative emotionality) and age 13 observed parenting (i.e., positive and negative behavior) and friendship (i.e., conflict and warmth), with multi-informant ratings of age 15 aggression and rule-breaking behavior. Negative parenting predicted growth in parent-rated EB, but only for adolescents with early reactive temperament. Temperament did not affect sensitivity to positive parenting or friendship. Results are discussed in the context of differential susceptibility theory and intervention implications for adolescents. © 2018 Society for Research on Adolescence.

[Analysis of related factors of slope plant hyperspectral remote sensing].

PubMed

Sun, Wei-Qi; Zhao, Yun-Sheng; Tu, Lin-Ling

2014-09-01

In the present paper, the slope gradient, aspect, detection zenith angle and plant types were analyzed. In order to strengthen the theoretical discussion, the research was under laboratory condition, and modeled uniform slope for slope plant. Through experiments we found that these factors indeed have influence on plant hyperspectral remote sensing. When choosing slope gradient as the variate, the blade reflection first increases and then decreases as the slope gradient changes from 0° to 36°; When keeping other factors constant, and only detection zenith angle increasing from 0° to 60°, the spectral characteristic of slope plants do not change significantly in visible light band, but decreases gradually in near infrared band; With only slope aspect changing, when the dome meets the light direction, the blade reflectance gets maximum, and when the dome meets the backlit direction, the blade reflectance gets minimum, furthermore, setting the line of vertical intersection of incidence plane and the dome as an axis, the reflectance on the axis's both sides shows symmetric distribution; In addition, spectral curves of different plant types have a lot differences between each other, which means that the plant types also affect hyperspectral remote sensing results of slope plants. This research breaks through the limitations of the traditional vertical remote sensing data collection and uses the multi-angle and hyperspectral information to analyze spectral characteristics of slope plants. So this research has theoretical significance to the development of quantitative remote sensing, and has application value to the plant remote sensing monitoring.

MultiNet TCP/P/IP for VAX/VMS update

NASA Technical Reports Server (NTRS)

Vance, L. Stuart

1991-01-01

Outlines of device support; DECnet interoperability; installation; MultiNet services; domain name server; Telnet; FTP; SMTP; DECwindows over TCP/IP; BSD r services; remote printing; RPC services and NFS server; NFS client; netcontrol; diagnostics; programming support; and MultiNet features are presented in viewgraph format.

Remoteness from sources of persistent organic pollutants in the multi-media global environment.

PubMed

Göktaş, Recep Kaya; MacLeod, Matthew

2016-10-01

Quantifying the remoteness from sources of persistent organic pollutants (POPs) can inform the design of monitoring studies and the interpretation of measurement data. Previous work on quantifying remoteness has not explicitly considered partitioning between the gas phase and aerosols, and between the atmosphere and the Earth's surface. The objective of this study is to present a metric of remoteness for POPs transported through the atmosphere calculated with a global multimedia fate model, BETR-Research. We calculated the remoteness of regions covering the entire globe from emission sources distributed according to light emissions, and taking into account the multimedia partitioning properties of chemicals and using averaged global climate data. Remoteness for hypothetical chemicals with distinct partitioning properties (volatile, semi-volatile, hydrophilic, low-volatility) and having two different half-lives in air (60-day and 2-day) are presented. Differences in remoteness distribution among the hypothetical chemicals are most pronounced in scenarios assuming 60-day half-life in air. In scenarios with a 2-day half-life in air, degradation dominates over wet and dry deposition processes as a pathway for atmospheric removal of all chemicals except the low-volatility chemical. The remoteness distribution of the low-volatility chemical is strongly dependent on assumptions about degradability on atmospheric aerosols. Calculations that considered seasonal variability in temperature, hydroxyl radical concentrations in the atmosphere and global atmospheric and oceanic circulation patterns indicate that variability in hydroxyl radical concentrations largely determines the seasonal variability of remoteness. Concentrations of polybrominated diphenyl ethers (PBDEs) measured in tree bark from around the world are more highly correlated with remoteness calculated using our methods than with proximity to human population, and we see considerable potential to apply remoteness calculations for interpretation of monitoring data collected under programs such as the Stockholm Convention Global Monitoring Plan. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Multi-Destination and Multi-Purpose Trip Effects in the Analysis of the Demand for Trips to a Remote Recreational Site

NASA Astrophysics Data System (ADS)

Martínez-Espiñeira, Roberto; Amoako-Tuffour, Joe

2009-06-01

One of the basic assumptions of the travel cost method for recreational demand analysis is that the travel cost is always incurred for a single purpose recreational trip. Several studies have skirted around the issue with simplifying assumptions and dropping observations considered as nonconventional holiday-makers or as nontraditional visitors from the sample. The effect of such simplifications on the benefit estimates remains conjectural. Given the remoteness of notable recreational parks, multi-destination or multi-purpose trips are not uncommon. This article examines the consequences of allocating travel costs to a recreational site when some trips were taken for purposes other than recreation and/or included visits to other recreational sites. Using a multi-purpose weighting approach on data from Gros Morne National Park, Canada, we conclude that a proper correction for multi-destination or multi-purpose trip is more of what is needed to avoid potential biases in the estimated effects of the price (travel-cost) variable and of the income variable in the trip generation equation.

Remote sensing of ecosystem health: opportunities, challenges, and future perspectives.

PubMed

Li, Zhaoqin; Xu, Dandan; Guo, Xulin

2014-11-07

Maintaining a healthy ecosystem is essential for maximizing sustainable ecological services of the best quality to human beings. Ecological and conservation research has provided a strong scientific background on identifying ecological health indicators and correspondingly making effective conservation plans. At the same time, ecologists have asserted a strong need for spatially explicit and temporally effective ecosystem health assessments based on remote sensing data. Currently, remote sensing of ecosystem health is only based on one ecosystem attribute: vigor, organization, or resilience. However, an effective ecosystem health assessment should be a comprehensive and dynamic measurement of the three attributes. This paper reviews opportunities of remote sensing, including optical, radar, and LiDAR, for directly estimating indicators of the three ecosystem attributes, discusses the main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system, and provides some future perspectives. The main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system are: (1) scale issue; (2) transportability issue; (3) data availability; and (4) uncertainties in health indicators estimated from remote sensing data. However, the Radarsat-2 constellation, upcoming new optical sensors on Worldview-3 and Sentinel-2 satellites, and improved technologies for the acquisition and processing of hyperspectral, multi-angle optical, radar, and LiDAR data and multi-sensoral data fusion may partly address the current challenges.

Remote sensing estimation of the total phosphorus concentration in a large lake using band combinations and regional multivariate statistical modeling techniques.

PubMed

Gao, Yongnian; Gao, Junfeng; Yin, Hongbin; Liu, Chuansheng; Xia, Ting; Wang, Jing; Huang, Qi

2015-03-15

Remote sensing has been widely used for ater quality monitoring, but most of these monitoring studies have only focused on a few water quality variables, such as chlorophyll-a, turbidity, and total suspended solids, which have typically been considered optically active variables. Remote sensing presents a challenge in estimating the phosphorus concentration in water. The total phosphorus (TP) in lakes has been estimated from remotely sensed observations, primarily using the simple individual band ratio or their natural logarithm and the statistical regression method based on the field TP data and the spectral reflectance. In this study, we investigated the possibility of establishing a spatial modeling scheme to estimate the TP concentration of a large lake from multi-spectral satellite imagery using band combinations and regional multivariate statistical modeling techniques, and we tested the applicability of the spatial modeling scheme. The results showed that HJ-1A CCD multi-spectral satellite imagery can be used to estimate the TP concentration in a lake. The correlation and regression analysis showed a highly significant positive relationship between the TP concentration and certain remotely sensed combination variables. The proposed modeling scheme had a higher accuracy for the TP concentration estimation in the large lake compared with the traditional individual band ratio method and the whole-lake scale regression-modeling scheme. The TP concentration values showed a clear spatial variability and were high in western Lake Chaohu and relatively low in eastern Lake Chaohu. The northernmost portion, the northeastern coastal zone and the southeastern portion of western Lake Chaohu had the highest TP concentrations, and the other regions had the lowest TP concentration values, except for the coastal zone of eastern Lake Chaohu. These results strongly suggested that the proposed modeling scheme, i.e., the band combinations and the regional multivariate statistical modeling techniques, demonstrated advantages for estimating the TP concentration in a large lake and had a strong potential for universal application for the TP concentration estimation in large lake waters worldwide. Copyright © 2014 Elsevier Ltd. All rights reserved.

A teleoperated system for remote site characterization

NASA Technical Reports Server (NTRS)

Sandness, Gerald A.; Richardson, Bradley S.; Pence, Jon

1994-01-01

The detection and characterization of buried objects and materials is an important step in the restoration of burial sites containing chemical and radioactive waste materials at Department of Energy (DOE) and Department of Defense (DOD) facilities. By performing these tasks with remotely controlled sensors, it is possible to obtain improved data quality and consistency as well as enhanced safety for on-site workers. Therefore, the DOE Office of Technology Development and the US Army Environmental Center have jointly supported the development of the Remote Characterization System (RCS). One of the main components of the RCS is a small remotely driven survey vehicle that can transport various combinations of geophysical and radiological sensors. Currently implemented sensors include ground-penetrating radar, magnetometers, an electromagnetic induction sensor, and a sodium iodide radiation detector. The survey vehicle was constructed predominantly of non-metallic materials to minimize its effect on the operation of its geophysical sensors. The system operator controls the vehicle from a remote, truck-mounted, base station. Video images are transmitted to the base station by a radio link to give the operator necessary visual information. Vehicle control commands, tracking information, and sensor data are transmitted between the survey vehicle and the base station by means of a radio ethernet link. Precise vehicle tracking coordinates are provided by a differential Global Positioning System (GPS).

Satellite remote sensing of landscape freeze/thaw state dynamics for complex Topography and Fire Disturbance Areas Using multi-sensor radar and SRTM digital elevation models

NASA Technical Reports Server (NTRS)

Podest, Erika; McDonald, Kyle; Kimball, John; Randerson, James

2003-01-01

We characterize differences in radar-derived freeze/thaw state, examining transitions over complex terrain and landscape disturbance regimes. In areas of complex terrain, we explore freezekhaw dynamics related to elevation, slope aspect and varying landcover. In the burned regions, we explore the timing of seasonal freeze/thaw transition as related to the recovering landscape, relative to that of a nearby control site. We apply in situ biophysical measurements, including flux tower measurements to validate and interpret the remotely sensed parameters. A multi-scale analysis is performed relating high-resolution SAR backscatter and moderate resolution scatterometer measurements to assess trade-offs in spatial and temporal resolution in the remotely sensed fields.

Multi-Pulse Excitation for Underwater Analysis of Copper-Based Alloys Using a Novel Remote Laser-Induced Breakdown Spectroscopy (LIBS) System.

PubMed

Guirado, Salvador; Fortes, Francisco J; Laserna, J Javier

2016-04-01

In this work, the use of multi-pulse excitation has been evaluated as an effective solution to mitigate the preferential ablation of the most volatile elements, namely Sn, Pb, and Zn, observed during laser-induced breakdown spectroscopy (LIBS) analysis of copper-based alloys. The novel remote LIBS prototype used in this experiments featured both single-pulse (SP-LIBS) and multi-pulse excitation (MP-LIBS). The remote instrument is capable of performing chemical analysis of submersed materials up to a depth of 50 m. Laser-induced breakdown spectroscopy analysis was performed at air pressure settings simulating the conditions during a real subsea analysis. A set of five certified bronze standards with variable concentration of Cu, As, Sn, Pb, and Zn were used. In SP-LIBS, signal emission is strongly sensitive to ambient pressure. In this case, fractionation effect was observed. Multi-pulse excitation circumvents the effect of pressure over the quantitative analysis, thus avoiding the fractionation phenomena observed in single pulse LIBS. The use of copper as internal standard minimizes matrix effects and discrepancies due to variation in ablated mass. © The Author(s) 2016.

Control Systems Cyber Security:Defense in Depth Strategies

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

David Kuipers; Mark Fabro

2006-05-01

Information infrastructures across many public and private domains share several common attributes regarding IT deployments and data communications. This is particularly true in the control systems domain. A majority of the systems use robust architectures to enhance business and reduce costs by increasing the integration of external, business, and control system networks. However, multi-network integration strategies often lead to vulnerabilities that greatly reduce the security of an organization, and can expose mission-critical control systems to cyber threats. This document provides guidance and direction for developing ‘defense-in-depth’ strategies for organizations that use control system networks while maintaining a multi-tier information architecturemore » that requires: Maintenance of various field devices, telemetry collection, and/or industrial-level process systems Access to facilities via remote data link or modem Public facing services for customer or corporate operations A robust business environment that requires connections among the control system domain, the external Internet, and other peer organizations.« less

Control Systems Cyber Security: Defense-in-Depth Strategies

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

Mark Fabro

2007-10-01

Information infrastructures across many public and private domains share several common attributes regarding IT deployments and data communications. This is particularly true in the control systems domain. A majority of the systems use robust architectures to enhance business and reduce costs by increasing the integration of external, business, and control system networks. However, multi-network integration strategies often lead to vulnerabilities that greatly reduce the security of an organization, and can expose mission-critical control systems to cyber threats. This document provides guidance and direction for developing ‘defense-in-depth’ strategies for organizations that use control system networks while maintaining a multi-tier information architecturemore » that requires: • Maintenance of various field devices, telemetry collection, and/or industrial-level process systems • Access to facilities via remote data link or modem • Public facing services for customer or corporate operations • A robust business environment that requires connections among the control system domain, the external Internet, and other peer organizations.« less

36 Years of Remote Oceanographic Laser Fluorosensing: Findings, Challenges and Pathways to Explore

NASA Astrophysics Data System (ADS)

Chekalyuk, A. M.

2009-12-01

Since its initial bright start in early 70s, the oceanographic applications of laser remote fluorosensing have been mostly driven by the enthusiastic laser geeks, who tried to transfer the recent technological advances from their laboratory breadboards to the real world. This communication provides an overview of the key milestones and advances in the oceanographic applications of remote laser fluorosensing that is used for qualitative and quantitative characterization of the key aquatic constituents, including chromophoric dissolved organic matter, phytoplankton pigments, their biomass, community structure, and photo-physiological status. The basic principles and analytical techniques, including fluorescence excitation and emission measurements, as well as active control over the media to retrieve additional information (“super-active remote sensing”), are briefly discussed and illustrated with examples of practical applications. The laser excitation sources (including solid state, tunable lasers and optical parametric oscillators) and signal detectors and analyzers (including multi-spectral and hyperspectral systems) are discussed. The advantages and limitations of various platforms (stationary settings, ships, airplanes, helicopters, unmanned autonomous vehicles (UAV), and satellites) are analyzed. The recent findings, methodological and technological developments in oceanographic applications of laser fluorescence indicate that there is a significant, still underexplored potential of remote fluorosensing that may provide new observational capabilities and serve as a useful tool for oceanographic research, bio-environmental monitoring, and validation of passive satellite retrievals.

Telerobot local-remote control architecture for space flight program applications

NASA Technical Reports Server (NTRS)

Zimmerman, Wayne; Backes, Paul; Steele, Robert; Long, Mark; Bon, Bruce; Beahan, John

1993-01-01

The JPL Supervisory Telerobotics (STELER) Laboratory has developed and demonstrated a unique local-remote robot control architecture which enables management of intermittent communication bus latencies and delays such as those expected for ground-remote operation of Space Station robotic systems via the Tracking and Data Relay Satellite System (TDRSS) communication platform. The current work at JPL in this area has focused on enhancing the technologies and transferring the control architecture to hardware and software environments which are more compatible with projected ground and space operational environments. At the local site, the operator updates the remote worksite model using stereo video and a model overlay/fitting algorithm which outputs the location and orientation of the object in free space. That information is relayed to the robot User Macro Interface (UMI) to enable programming of the robot control macros. This capability runs on a single Silicon Graphics Inc. machine. The operator can employ either manual teleoperation, shared control, or supervised autonomous control to manipulate the intended object. The remote site controller, called the Modular Telerobot Task Execution System (MOTES), runs in a multi-processor VME environment and performs the task sequencing, task execution, trajectory generation, closed loop force/torque control, task parameter monitoring, and reflex action. This paper describes the new STELER architecture implementation, and also documents the results of the recent autonomous docking task execution using the local site and MOTES.

Change Detection in High-Resolution Remote Sensing Images Using Levene-Test and Fuzzy Evaluation

NASA Astrophysics Data System (ADS)

Wang, G. H.; Wang, H. B.; Fan, W. F.; Liu, Y.; Liu, H. J.

2018-04-01

High-resolution remote sensing images possess complex spatial structure and rich texture information, according to these, this paper presents a new method of change detection based on Levene-Test and Fuzzy Evaluation. It first got map-spots by segmenting two overlapping images which had been pretreated, extracted features such as spectrum and texture. Then, changed information of all map-spots which had been treated by the Levene-Test were counted to obtain the candidate changed regions, hue information (H component) was extracted through the IHS Transform and conducted change vector analysis combined with the texture information. Eventually, the threshold was confirmed by an iteration method, the subject degrees of candidate changed regions were calculated, and final change regions were determined. In this paper experimental results on multi-temporal ZY-3 high-resolution images of some area in Jiangsu Province show that: Through extracting map-spots of larger difference as the candidate changed regions, Levene-Test decreases the computing load, improves the precision of change detection, and shows better fault-tolerant capacity for those unchanged regions which are of relatively large differences. The combination of Hue-texture features and fuzzy evaluation method can effectively decrease omissions and deficiencies, improve the precision of change detection.

Can we infer plant facilitation from remote sensing? A test across global drylands

PubMed Central

Xu, Chi; Holmgren, Milena; Van Nes, Egbert H.; Maestre, Fernando T.; Soliveres, Santiago; Berdugo, Miguel; Kéfi, Sonia; Marquet, Pablo A.; Abades, Sebastian; Scheffer, Marten

2016-01-01

Facilitation is a major force shaping the structure and diversity of plant communities in terrestrial ecosystems. Detecting positive plant-plant interactions relies on the combination of field experimentation and the demonstration of spatial association between neighboring plants. This has often restricted the study of facilitation to particular sites, limiting the development of systematic assessments of facilitation over regional and global scales. Here we explore whether the frequency of plant spatial associations detected from high-resolution remotely-sensed images can be used to infer plant facilitation at the community level in drylands around the globe. We correlated the information from remotely-sensed images freely available through Google Earth™ with detailed field assessments, and used a simple individual-based model to generate patch-size distributions using different assumptions about the type and strength of plant-plant interactions. Most of the patterns found from the remotely-sensed images were more right-skewed than the patterns from the null model simulating a random distribution. This suggests that the plants in the studied drylands show stronger spatial clustering than expected by chance. We found that positive plant co-occurrence, as measured in the field, was significantly related to the skewness of vegetation patch-size distribution measured using Google Earth™ images. Our findings suggest that the relative frequency of facilitation may be inferred from spatial pattern signals measured from remotely-sensed images, since facilitation often determines positive co-occurrence among neighboring plants. They pave the road for a systematic global assessment of the role of facilitation in terrestrial ecosystems. PMID:26552256

A Short Tutorial on Inertial Navigation System and Global Positioning System Integration

NASA Technical Reports Server (NTRS)

Smalling, Kyle M.; Eure, Kenneth W.

2015-01-01

The purpose of this document is to describe a simple method of integrating Inertial Navigation System (INS) information with Global Positioning System (GPS) information for an improved estimate of vehicle attitude and position. A simple two dimensional (2D) case is considered. The attitude estimates are derived from sensor data and used in the estimation of vehicle position and velocity through dead reckoning within the INS. The INS estimates are updated with GPS estimates using a Kalman filter. This tutorial is intended for the novice user with a focus on bringing the reader from raw sensor measurements to an integrated position and attitude estimate. An application is given using a remotely controlled ground vehicle operating in assumed 2D environment. The theory is developed first followed by an illustrative example.

Assessing regional environmental quality by integrated use of remote sensing, GIS, and spatial multi-criteria evaluation for prioritization of environmental restoration.

PubMed

Rahman, Md Rejaur; Shi, Z H; Chongfa, Cai

2014-11-01

This study was an attempt to analyse the regional environmental quality with the application of remote sensing, geographical information system, and spatial multiple criteria decision analysis and, to project a quantitative method applicable to identify the status of the regional environment of the study area. Using spatial multi-criteria evaluation (SMCE) approach with expert knowledge in this study, an integrated regional environmental quality index (REQI) was computed and classified into five levels of regional environment quality viz. worse, poor, moderate, good, and very good. During the process, a set of spatial criteria were selected (here, 15 criterions) together with the degree of importance of criteria in sustainability of the regional environment. Integrated remote sensing and GIS technique and models were applied to generate the necessary factors (criterions) maps for the SMCE approach. The ranking, along with expected value method, was used to standardize the factors and on the other hand, an analytical hierarchy process (AHP) was applied for calculating factor weights. The entire process was executed in the integrated land and water information system (ILWIS) software tool that supports SMCE. The analysis showed that the overall regional environmental quality of the area was at moderate level and was partly determined by elevation. Areas under worse and poor quality of environment indicated that the regional environmental status showed decline in these parts of the county. The study also revealed that the human activities, vegetation condition, soil erosion, topography, climate, and soil conditions have serious influence on the regional environment condition of the area. Considering the regional characteristics of environmental quality, priority, and practical needs for environmental restoration, the study area was further regionalized into four priority areas which may serve as base areas of decision making for the recovery, rebuilding, and protection of the environment.

A component-based system for agricultural drought monitoring by remote sensing.

PubMed

Dong, Heng; Li, Jun; Yuan, Yanbin; You, Lin; Chen, Chao

2017-01-01

In recent decades, various kinds of remote sensing-based drought indexes have been proposed and widely used in the field of drought monitoring. However, the drought-related software and platform development lag behind the theoretical research. The current drought monitoring systems focus mainly on information management and publishing, and cannot implement professional drought monitoring or parameter inversion modelling, especially the models based on multi-dimensional feature space. In view of the above problems, this paper aims at fixing this gap with a component-based system named RSDMS to facilitate the application of drought monitoring by remote sensing. The system is designed and developed based on Component Object Model (COM) to ensure the flexibility and extendibility of modules. RSDMS realizes general image-related functions such as data management, image display, spatial reference management, image processing and analysis, and further provides drought monitoring and evaluation functions based on internal and external models. Finally, China's Ningxia region is selected as the study area to validate the performance of RSDMS. The experimental results show that RSDMS provide an efficient and scalable support to agricultural drought monitoring.

A component-based system for agricultural drought monitoring by remote sensing

PubMed Central

Yuan, Yanbin; You, Lin; Chen, Chao

2017-01-01

In recent decades, various kinds of remote sensing-based drought indexes have been proposed and widely used in the field of drought monitoring. However, the drought-related software and platform development lag behind the theoretical research. The current drought monitoring systems focus mainly on information management and publishing, and cannot implement professional drought monitoring or parameter inversion modelling, especially the models based on multi-dimensional feature space. In view of the above problems, this paper aims at fixing this gap with a component-based system named RSDMS to facilitate the application of drought monitoring by remote sensing. The system is designed and developed based on Component Object Model (COM) to ensure the flexibility and extendibility of modules. RSDMS realizes general image-related functions such as data management, image display, spatial reference management, image processing and analysis, and further provides drought monitoring and evaluation functions based on internal and external models. Finally, China’s Ningxia region is selected as the study area to validate the performance of RSDMS. The experimental results show that RSDMS provide an efficient and scalable support to agricultural drought monitoring. PMID:29236700

FRR: fair remote retrieval of outsourced private medical records in electronic health networks.

PubMed

Wang, Huaqun; Wu, Qianhong; Qin, Bo; Domingo-Ferrer, Josep

2014-08-01

Cloud computing is emerging as the next-generation IT architecture. However, cloud computing also raises security and privacy concerns since the users have no physical control over the outsourced data. This paper focuses on fairly retrieving encrypted private medical records outsourced to remote untrusted cloud servers in the case of medical accidents and disputes. Our goal is to enable an independent committee to fairly recover the original private medical records so that medical investigation can be carried out in a convincing way. We achieve this goal with a fair remote retrieval (FRR) model in which either t investigation committee members cooperatively retrieve the original medical data or none of them can get any information on the medical records. We realize the first FRR scheme by exploiting fair multi-member key exchange and homomorphic privately verifiable tags. Based on the standard computational Diffie-Hellman (CDH) assumption, our scheme is provably secure in the random oracle model (ROM). A detailed performance analysis and experimental results show that our scheme is efficient in terms of communication and computation. Copyright © 2014 Elsevier Inc. All rights reserved.

Feature extraction based on extended multi-attribute profiles and sparse autoencoder for remote sensing image classification

NASA Astrophysics Data System (ADS)

Teffahi, Hanane; Yao, Hongxun; Belabid, Nasreddine; Chaib, Souleyman

2018-02-01

The satellite images with very high spatial resolution have been recently widely used in image classification topic as it has become challenging task in remote sensing field. Due to a number of limitations such as the redundancy of features and the high dimensionality of the data, different classification methods have been proposed for remote sensing images classification particularly the methods using feature extraction techniques. This paper propose a simple efficient method exploiting the capability of extended multi-attribute profiles (EMAP) with sparse autoencoder (SAE) for remote sensing image classification. The proposed method is used to classify various remote sensing datasets including hyperspectral and multispectral images by extracting spatial and spectral features based on the combination of EMAP and SAE by linking them to kernel support vector machine (SVM) for classification. Experiments on new hyperspectral image "Huston data" and multispectral image "Washington DC data" shows that this new scheme can achieve better performance of feature learning than the primitive features, traditional classifiers and ordinary autoencoder and has huge potential to achieve higher accuracy for classification in short running time.

Geospatial Technologies and Higher Education in Argentina

ERIC Educational Resources Information Center

Leguizamon, Saturnino

2010-01-01

The term "geospatial technologies" encompasses a large area of fields involving cartography, spatial analysis, geographic information system, remote sensing, global positioning systems and many others. These technologies should be expected to be available (as "natural tools") for a country with a large surface and a variety of…

Multi-scale remote sensing of coral reefs

USGS Publications Warehouse

Andréfouët, Serge; Hochberg, E.J.; Chevillon, Christophe; Muller-Karger, Frank E.; Brock, John C.; Hu, Chuanmin

2005-01-01

In this chapter we present how both direct and indirect remote sensing can be integrated to address two major coral reef applications - coral bleaching and assessment of biodiversity. This approach reflects the current non-linear integration of remote sensing for environmental assessment of coral reefs, resulting from a rapid increase in available sensors, processing methods and interdisciplinary collaborations (Andréfouët and Riegl, 2004). Moreover, this approach has greatly benefited from recent collaborations of once independent investigations (e.g., benthic ecology, remote sensing, and numerical modeling).

SMERGE: A multi-decadal root-zone soil moisture product for CONUS

NASA Astrophysics Data System (ADS)

Crow, W. T.; Dong, J.; Tobin, K. J.; Torres, R.

2017-12-01

Multi-decadal root-zone soil moisture products are of value for a range of water resource and climate applications. The NASA-funded root-zone soil moisture merging project (SMERGE) seeks to develop such products through the optimal merging of land surface model predictions with surface soil moisture retrievals acquired from multi-sensor remote sensing products. This presentation will describe the creation and validation of a daily, multi-decadal (1979-2015), vertically-integrated (both surface to 40 cm and surface to 100 cm), 0.125-degree root-zone product over the contiguous United States (CONUS). The modeling backbone of the system is based on hourly root-zone soil moisture simulations generated by the Noah model (v3.2) operating within the North American Land Data Assimilation System (NLDAS-2). Remotely-sensed surface soil moisture retrievals are taken from the multi-sensor European Space Agency Climate Change Initiative soil moisture data set (ESA CCI SM). In particular, the talk will detail: 1) the exponential smoothing approach used to convert surface ESA CCI SM retrievals into root-zone soil moisture estimates, 2) the averaging technique applied to merge (temporally-sporadic) remotely-sensed with (continuous) NLDAS-2 land surface model estimates of root-zone soil moisture into the unified SMERGE product, and 3) the validation of the SMERGE product using long-term, ground-based soil moisture datasets available within CONUS.

Research on orbit prediction for solar-based calibration proper satellite

NASA Astrophysics Data System (ADS)

Chen, Xuan; Qi, Wenwen; Xu, Peng

2018-03-01

Utilizing the mathematical model of the orbit mechanics, the orbit prediction is to forecast the space target's orbit information of a certain time based on the orbit of the initial moment. The proper satellite radiometric calibration and calibration orbit prediction process are introduced briefly. On the basis of the research of the calibration space position design method and the radiative transfer model, an orbit prediction method for proper satellite radiometric calibration is proposed to select the appropriate calibration arc for the remote sensor and to predict the orbit information of the proper satellite and the remote sensor. By analyzing the orbit constraint of the proper satellite calibration, the GF-1solar synchronous orbit is chose as the proper satellite orbit in order to simulate the calibration visible durance for different satellites to be calibrated. The results of simulation and analysis provide the basis for the improvement of the radiometric calibration accuracy of the satellite remote sensor, which lays the foundation for the high precision and high frequency radiometric calibration.

Acoustic water bottom investigation with a remotely operated watercraft survey system

NASA Astrophysics Data System (ADS)

Yamasaki, Shintaro; Tabusa, Tomonori; Iwasaki, Shunsuke; Hiramatsu, Masahiro

2017-12-01

This paper describes a remotely operated investigation system developed by combining a modern leisure-use fish finder and an unmanned watercraft to survey water bottom topography and other data related to bottom materials. Current leisure-use fish finders have strong depth sounding capabilities and can provide precise sonar images and bathymetric information. Because these sonar instruments are lightweight and small, they can be used on unmanned small watercraft. With the developed system, an operator can direct the heading of an unmanned watercraft and monitor a PC display showing real-time positioning information through the use of onboard equipment and long-distance communication devices. Here, we explain how the system was developed and demonstrate the use of the system in an area of submerged woods in a lake. The system is low cost, easy to use, and mobile. It should be useful in surveying areas that have heretofore been hard to investigate, including remote, small, and shallow lakes, for example, volcanic and glacial lakes.

Intraoperative Cochlear Implant Device Testing Utilizing an Automated Remote System: A Prospective Pilot Study.

PubMed

Lohmann, Amanda R; Carlson, Matthew L; Sladen, Douglas P

2018-03-01

Intraoperative cochlear implant device testing provides valuable information regarding device integrity, electrode position, and may assist with determining initial stimulation settings. Manual intraoperative device testing during cochlear implantation requires the time and expertise of a trained audiologist. The purpose of the current study is to investigate the feasibility of using automated remote intraoperative cochlear implant reverse telemetry testing as an alternative to standard testing. Prospective pilot study evaluating intraoperative remote automated impedance and Automatic Neural Response Telemetry (AutoNRT) testing in 34 consecutive cochlear implant surgeries using the Intraoperative Remote Assistant (Cochlear Nucleus CR120). In all cases, remote intraoperative device testing was performed by trained operating room staff. A comparison was made to the "gold standard" of manual testing by an experienced cochlear implant audiologist. Electrode position and absence of tip fold-over was confirmed using plain film x-ray. Automated remote reverse telemetry testing was successfully completed in all patients. Intraoperative x-ray demonstrated normal electrode position without tip fold-over. Average impedance values were significantly higher using standard testing versus CR120 remote testing (standard mean 10.7 kΩ, SD 1.2 vs. CR120 mean 7.5 kΩ, SD 0.7, p < 0.001). There was strong agreement between standard manual testing and remote automated testing with regard to the presence of open or short circuits along the array. There were, however, two cases in which standard testing identified an open circuit, when CR120 testing showed the circuit to be closed. Neural responses were successfully obtained in all patients using both systems. There was no difference in basal electrode responses (standard mean 195.0 μV, SD 14.10 vs. CR120 194.5 μV, SD 14.23; p = 0.7814); however, more favorable (lower μV amplitude) results were obtained with the remote automated system in the apical 10 electrodes (standard 185.4 μV, SD 11.69 vs. CR120 177.0 μV, SD 11.57; p value < 0.001). These preliminary data demonstrate that intraoperative cochlear implant device testing using a remote automated system is feasible. This system may be useful for cochlear implant programs with limited audiology support or for programs looking to streamline intraoperative device testing protocols. Future studies with larger patient enrollment are required to validate these promising, but preliminary, findings.

[Investigation on remote measurement of air pollution by a method of infrared passive scanning imaging].

PubMed

Jiao, Yang; Xu, Liang; Gao, Min-Guang; Feng, Ming-Chun; Jin, Ling; Tong, Jing-Jing; Li, Sheng

2012-07-01

Passive remote sensing by Fourier-transform infrared (FTIR) spectrometry allows detection of air pollution. However, for the localization of a leak and a complete assessment of the situation in the case of the release of a hazardous cloud, information about the position and the distribution of a cloud is essential. Therefore, an imaging passive remote sensing system comprising an interferometer, a data acquisition and processing software, scan system, a video system, and a personal computer has been developed. The remote sensing of SF6 was done. The column densities of all directions in which a target compound has been identified may be retrieved by a nonlinear least squares fitting algorithm and algorithm of radiation transfer, and a false color image is displayed. The results were visualized by a video image, overlaid by false color concentration distribution image. The system has a high selectivity, and allows visualization and quantification of pollutant clouds.

Potential of Sentinel Satellites for Schistosomiasis Monitoring

NASA Astrophysics Data System (ADS)

Li, C.-R.; Tang, L.-L.; Niu, H.-B.; Zhou, X.-N.; Liu, Z.-Y.; Ma, L.-L.; Zhou, Y.-S.

2012-04-01

Schistosomiasis is a parasitic disease that menaces human health. In terms of impact this disease is second only to malaria as the most devastating parasitic disease. Oncomelania hupensis is the unique intermediate host of Schistosoma, and hence monitoring and controlling of the number of oncomelania is key to reduce the risk of schistosomiasis transmission. Remote sensing technology can real-timely access the large-scale environmental factors related to oncomelania breeding and reproduction, such as temperature, moisture, vegetation, soil, and rainfall, and can also provide the efficient information to determine the location, area, and spread tendency of oncomelania. Many studies show that the correlation coefficient between oncomelania densities and remote sensing environmental factors depends largely on suitable and high quality remote sensing data used in retrieve environmental factors. Research achievements on retrieving environmental factors (which are related to the living, multiplying and transmission of oncomelania) by multi-source remote data are shown firstly, including: (a) Vegetation information (e.g., Modified Soil-Adjusted Vegetation Index, Normalized Difference Moisture Index, Fractional Vegetation Cover) extracted from optical remote sensing data, such as Landsat TM, HJ-1A/HSI image; (b) Surface temperature retrieval from Thermal Infrared (TIR) and passive-microwave remote sensing data; (c) Water region, soil moisture, forest height retrieval from synthetic aperture radar data, such as Envisat SAR, DLR's ESAR image. Base on which, the requirements of environmental factor accuracy for schistosomiasis monitoring will be analyzed and summarized. Our work on applying remote sensing technique to schistosomiasis monitoring is then presented. The fuzzy information theory is employed to analyze the sensitivity and feasibility relation between oncomelania densities and environmental factors. Then a mechanism model of predicting oncomelania distribution and densities is developed. The new model is validated with field data of Dongting Lake and the dynamic monitoring of schistosomiasis breeding in Dongting Lake region is presented. Finally, emphasis are placed on analyzing the potential of Sentinel satellites for schistosomiasis monitoring. The requirements of optical high resolution data on spectral resolution, spatial resolution, radiometric resolution/accuracy, as well as the requirements of synthetic aperture radar data on operation frequency, spatial resolution, polarization, radiometric accuracy, repeat cycle are presented and then compared with the parameters of Sentinel satellites. The parameters of Sentinel satellites are also compared with those of available remote satellites, such as Envisat, Landsat, whose data are being used for schistosomiasis monitoring. The application potential of Sentinel satellites for the schistosomiasis monitoring will be concluded in the end, which will benefit for the mission operation, model development, etc.

Retrieval of Vertical Aerosol and Trace Gas Distributions from Polarization Sensitive Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS)

NASA Astrophysics Data System (ADS)

Tirpitz, Jan-Lukas; Friess, Udo; Platt, Ulrich

2017-04-01

An accurate knowledge of the vertical distribution of trace gases and aerosols is crucial for our understanding of the chemical and dynamical processes in the lower troposphere. Their accurate determination is typically only possible by means of laborious and expensive airborne in-situ measurements but in the recent decades, numerous promising ground-based remote sensing approaches have been developed. One of them is to infer vertical distributions from "Differential Optical Absorption Spectroscopy" (DOAS) measurements. DOAS is a technique to analyze UV- and visible radiation spectra of direct or scattered sunlight, which delivers information on different atmospheric parameters, integrated over the light path from space to the instrument. An appropriate set of DOAS measurements, recorded under different viewing directions (Multi-Axis DOAS) and thus different light path geometries, provides information on the atmospheric state. The vertical profiles of aerosol properties and trace gas concentrations can be retrieved from such a set by numerical inversion techniques, incorporating radiative transfer models. The information content of measured data is rarely sufficient for a well-constrained retrieval, particularly for atmospheric layers above 1 km. We showed in first simulations that, apart from spectral properties, the polarization state of skylight is likely to provide a significant amount of additional information on the atmospheric state and thus to enhance retrieval quality. We present first simulations, expectations and ideas on how to implement and characterize a polarization sensitive Multi-Axis DOAS instrument and a corresponding profile retrieval algorithm.

A Multi-User Remote Academic Laboratory System

ERIC Educational Resources Information Center

Barrios, Arquimedes; Panche, Stifen; Duque, Mauricio; Grisales, Victor H.; Prieto, Flavio; Villa, Jose L.; Chevrel, Philippe; Canu, Michael

2013-01-01

This article describes the development, implementation and preliminary operation assessment of Multiuser Network Architecture to integrate a number of Remote Academic Laboratories for educational purposes on automatic control. Through the Internet, real processes or physical experiments conducted at the control engineering laboratories of four…

Multi-party Semi-quantum Key Agreement with Delegating Quantum Computation

NASA Astrophysics Data System (ADS)

Liu, Wen-Jie; Chen, Zhen-Yu; Ji, Sai; Wang, Hai-Bin; Zhang, Jun

2017-10-01

A multi-party semi-quantum key agreement (SQKA) protocol based on delegating quantum computation (DQC) model is proposed by taking Bell states as quantum resources. In the proposed protocol, the participants only need the ability of accessing quantum channel and preparing single photons {|0〉, |1〉, |+〉, |-〉}, while the complicated quantum operations, such as the unitary operations and Bell measurement, will be delegated to the remote quantum center. Compared with previous quantum key agreement protocols, this client-server model is more feasible in the early days of the emergence of quantum computers. In order to prevent the attacks from outside eavesdroppers, inner participants and quantum center, two single photon sequences are randomly inserted into Bell states: the first sequence is used to perform the quantum channel detection, while the second is applied to disorder the positions of message qubits, which guarantees the security of the protocol.

Track-to-track association for object matching in an inter-vehicle communication system

NASA Astrophysics Data System (ADS)

Yuan, Ting; Roth, Tobias; Chen, Qi; Breu, Jakob; Bogdanovic, Miro; Weiss, Christian A.

2015-09-01

Autonomous driving poses unique challenges for vehicle environment perception due to the complex driving environment the autonomous vehicle finds itself in and differentiates from remote vehicles. Due to inherent uncertainty of the traffic environments and incomplete knowledge due to sensor limitation, an autonomous driving system using only local onboard sensor information is generally not sufficiently enough for conducting a reliable intelligent driving with guaranteed safety. In order to overcome limitations of the local (host) vehicle sensing system and to increase the likelihood of correct detections and classifications, collaborative information from cooperative remote vehicles could substantially facilitate effectiveness of vehicle decision making process. Dedicated Short Range Communication (DSRC) system provides a powerful inter-vehicle wireless communication channel to enhance host vehicle environment perceiving capability with the aid of transmitted information from remote vehicles. However, there is a major challenge before one can fuse the DSRC-transmitted remote information and host vehicle Radar-observed information (in the present case): the remote DRSC data must be correctly associated with the corresponding onboard Radar data; namely, an object matching problem. Direct raw data association (i.e., measurement-to-measurement association - M2MA) is straightforward but error-prone, due to inherent uncertain nature of the observation data. The uncertainties could lead to serious difficulty in matching decision, especially, using non-stationary data. In this study, we present an object matching algorithm based on track-to-track association (T2TA) and evaluate the proposed approach with prototype vehicles in real traffic scenarios. To fully exploit potential of the DSRC system, only GPS position data from remote vehicle are used in fusion center (at host vehicle), i.e., we try to get what we need from the least amount of information; additional feature information can help the data association but are not currently considered. Comparing to M2MA, benefits of the T2TA object matching approach are: i) tracks taking into account important statistical information can provide more reliable inference results; ii) the track-formed smoothed trajectories can be used for an easier shape matching; iii) each local vehicle can design its own tracker and sends only tracks to fusion center to alleviate communication constraints. A real traffic study with different driving environments, based on a statistical hypothesis test, shows promising object matching results of significant practical implications.

An Improvement of Robust Biometrics-Based Authentication and Key Agreement Scheme for Multi-Server Environments Using Smart Cards.

PubMed

Moon, Jongho; Choi, Younsung; Jung, Jaewook; Won, Dongho

2015-01-01

In multi-server environments, user authentication is a very important issue because it provides the authorization that enables users to access their data and services; furthermore, remote user authentication schemes for multi-server environments have solved the problem that has arisen from user's management of different identities and passwords. For this reason, numerous user authentication schemes that are designed for multi-server environments have been proposed over recent years. In 2015, Lu et al. improved upon Mishra et al.'s scheme, claiming that their remote user authentication scheme is more secure and practical; however, we found that Lu et al.'s scheme is still insecure and incorrect. In this paper, we demonstrate that Lu et al.'s scheme is vulnerable to outsider attack and user impersonation attack, and we propose a new biometrics-based scheme for authentication and key agreement that can be used in multi-server environments; then, we show that our proposed scheme is more secure and supports the required security properties.

Multi-temporal RADARSAT-1 and ERS backscattering signatures of coastal wetlands in southeastern Louisiana

USGS Publications Warehouse

Kwoun, Oh-Ig; Lu, Z.

2009-01-01

Using multi-temporal European Remote-sensing Satellites (ERS-1/-2) and Canadian Radar Satellite (RADARSAT-1) synthetic aperture radar (SAR) data over the Louisiana coastal zone, we characterize seasonal variations of radar backscat-tering according to vegetation type. Our main findings are as follows. First, ERS-1/-2 and RADARSAT-1 require careful radiometric calibration to perform multi-temporal backscattering analysis for wetland mapping. We use SAR backscattering signals from cities for the relative calibration. Second, using seasonally averaged backscattering coefficients from ERS-1/-2 and RADARSAT-1, we can differentiate most forests (bottomland and swamp forests) and marshes (freshwater, intermediate, brackish, and saline marshes) in coastal wetlands. The student t-test results support the usefulness of season-averaged backscatter data for classification. Third, combining SAR backscattering coefficients and an optical-sensor-based normalized difference vegetation index can provide further insight into vegetation type and enhance the separation between forests and marshes. Our study demonstrates that SAR can provide necessary information to characterize coastal wetlands and monitor their changes.

Identification of saline soils with multi-year remote sensing of crop yields

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

Lobell, D; Ortiz-Monasterio, I; Gurrola, F C

2006-10-17

Soil salinity is an important constraint to agricultural sustainability, but accurate information on its variation across agricultural regions or its impact on regional crop productivity remains sparse. We evaluated the relationships between remotely sensed wheat yields and salinity in an irrigation district in the Colorado River Delta Region. The goals of this study were to (1) document the relative importance of salinity as a constraint to regional wheat production and (2) develop techniques to accurately identify saline fields. Estimates of wheat yield from six years of Landsat data agreed well with ground-based records on individual fields (R{sup 2} = 0.65).more » Salinity measurements on 122 randomly selected fields revealed that average 0-60 cm salinity levels > 4 dS m{sup -1} reduced wheat yields, but the relative scarcity of such fields resulted in less than 1% regional yield loss attributable to salinity. Moreover, low yield was not a reliable indicator of high salinity, because many other factors contributed to yield variability in individual years. However, temporal analysis of yield images showed a significant fraction of fields exhibited consistently low yields over the six year period. A subsequent survey of 60 additional fields, half of which were consistently low yielding, revealed that this targeted subset had significantly higher salinity at 30-60 cm depth than the control group (p = 0.02). These results suggest that high subsurface salinity is associated with consistently low yields in this region, and that multi-year yield maps derived from remote sensing therefore provide an opportunity to map salinity across agricultural regions.« less

a Data Field Method for Urban Remotely Sensed Imagery Classification Considering Spatial Correlation

NASA Astrophysics Data System (ADS)

Zhang, Y.; Qin, K.; Zeng, C.; Zhang, E. B.; Yue, M. X.; Tong, X.

2016-06-01

Spatial correlation between pixels is important information for remotely sensed imagery classification. Data field method and spatial autocorrelation statistics have been utilized to describe and model spatial information of local pixels. The original data field method can represent the spatial interactions of neighbourhood pixels effectively. However, its focus on measuring the grey level change between the central pixel and the neighbourhood pixels results in exaggerating the contribution of the central pixel to the whole local window. Besides, Geary's C has also been proven to well characterise and qualify the spatial correlation between each pixel and its neighbourhood pixels. But the extracted object is badly delineated with the distracting salt-and-pepper effect of isolated misclassified pixels. To correct this defect, we introduce the data field method for filtering and noise limitation. Moreover, the original data field method is enhanced by considering each pixel in the window as the central pixel to compute statistical characteristics between it and its neighbourhood pixels. The last step employs a support vector machine (SVM) for the classification of multi-features (e.g. the spectral feature and spatial correlation feature). In order to validate the effectiveness of the developed method, experiments are conducted on different remotely sensed images containing multiple complex object classes inside. The results show that the developed method outperforms the traditional method in terms of classification accuracies.

Detection of geothermal anomalies in Tengchong, Yunnan Province, China from MODIS multi-temporal night LST imagery

NASA Astrophysics Data System (ADS)

Li, H.; Kusky, T. M.; Peng, S.; Zhu, M.

2012-12-01

Thermal infrared (TIR) remote sensing is an important technique in the exploration of geothermal resources. In this study, a geothermal survey is conducted in Tengchong area of Yunnan province in China using multi-temporal MODIS LST (Land Surface Temperature). The monthly night MODIS LST data from Mar. 2000 to Mar. 2011 of the study area were collected and analyzed. The 132 month average LST map was derived and three geothermal anomalies were identified. The findings of this study agree well with the results from relative geothermal gradient measurements. Finally, we conclude that TIR remote sensing is a cost-effective technique to detect geothermal anomalies. Combining TIR remote sensing with geological analysis and the understanding of geothermal mechanism is an accurate and efficient approach to geothermal area detection.

Empirical validation and proof of added value of MUSICA's tropospheric δD remote sensing products

NASA Astrophysics Data System (ADS)

Schneider, M.; González, Y.; Dyroff, C.; Christner, E.; Wiegele, A.; Barthlott, S.; García, O. E.; Sepúlveda, E.; Hase, F.; Andrey, J.; Blumenstock, T.; Guirado, C.; Ramos, R.; Rodríguez, S.

2015-01-01

The project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) integrates tropospheric water vapour isotopologue remote sensing and in situ observations. This paper presents a first empirical validation of MUSICA's H2O and δD remote sensing products, generated from ground-based FTIR (Fourier transform infrared), spectrometer and space-based IASI (infrared atmospheric sounding interferometer) observation. The study is made in the area of the Canary Islands in the subtropical northern Atlantic. As reference we use well calibrated in situ measurements made aboard an aircraft (between 200 and 6800 m a.s.l.) by the dedicated ISOWAT instrument and on the island of Tenerife at two different altitudes (at Izaña, 2370 m a.s.l., and at Teide, 3550 m a.s.l.) by two commercial Picarro L2120-i water isotopologue analysers. The comparison to the ISOWAT profile measurements shows that the remote sensors can well capture the variations in the water vapour isotopologues, and the scatter with respect to the in situ references suggests a δD random uncertainty for the FTIR product of much better than 45‰ in the lower troposphere and of about 15‰ for the middle troposphere. For the middle tropospheric IASI δD product the study suggests a respective uncertainty of about 15‰. In both remote sensing data sets we find a positive δD bias of 30-70‰. Complementing H2O observations with δD data allows moisture transport studies that are not possible with H2O observations alone. We are able to qualitatively demonstrate the added value of the MUSICA δD remote sensing data. We document that the δD-H2O curves obtained from the different in situ and remote sensing data sets (ISOWAT, Picarro at Izaña and Teide, FTIR, and IASI) consistently identify two different moisture transport pathways to the subtropical north eastern Atlantic free troposphere.

Mapping the Future Today: The Community College of Baltimore County Geospatial Applications Program

ERIC Educational Resources Information Center

Jeffrey, Scott; Alvarez, Jaime

2010-01-01

The Geospatial Applications Program at the Community College of Baltimore County (CCBC), located five miles west of downtown Baltimore, Maryland, provides comprehensive instruction in geographic information systems (GIS), remote sensing and global positioning systems (GPS). Geospatial techniques, which include computer-based mapping and remote…

Geospatial Technologies: Real Projects in Real Classrooms

ERIC Educational Resources Information Center

Kolvoord, Bob

2008-01-01

Geospatial technologies of geographic information systems, global positioning systems, and remote sensing are just a few of the projects that evoke an unexpected drive and devotion from high school students in Virginia. Their integration into different curricular areas lets students focus on understanding their community and the many issues that…

Uses of GIS for Homeland Security and Emergency Management for Higher Education Institutions

ERIC Educational Resources Information Center

Murchison, Stuart B.

2010-01-01

Geographic information systems (GIS) are a major component of the geospatial sciences, which are also composed of geostatistical analysis, remote sensing, and global positional satellite systems. These systems can be integrated into GIS for georeferencing, pattern analysis, visualization, and understanding spatial concepts that transcend…

MISR at 15: Multiple Perspectives on Our Changing Earth

NASA Astrophysics Data System (ADS)

Diner, D. J.; Ackerman, T. P.; Braverman, A. J.; Bruegge, C. J.; Chopping, M. J.; Clothiaux, E. E.; Davies, R.; Di Girolamo, L.; Garay, M. J.; Jovanovic, V. M.; Kahn, R. A.; Kalashnikova, O.; Knyazikhin, Y.; Liu, Y.; Marchand, R.; Martonchik, J. V.; Muller, J. P.; Nolin, A. W.; Pinty, B.; Verstraete, M. M.; Wu, D. L.

2014-12-01

Launched aboard NASA's Terra satellite in December 1999, the Multi-angle Imaging SpectroRadiometer (MISR) instrument has opened new vistas in remote sensing of our home planet. Its 9 pushbroom cameras provide as many view angles ranging from 70 degrees forward to 70 degrees backward along Terra's flight track, in four visible and near-infrared spectral bands. MISR's well-calibrated, accurately co-registered, and moderately high spatial resolution radiance images have been coupled with novel data processing algorithms to mine the information content of angular reflectance anisotropy and multi-camera stereophotogrammetry, enabling new perspectives on the 3-D structure and dynamics of Earth's atmosphere and surface in support of climate and environmental research. Beginning with "first light" in February 2000, the nearly 15-year (and counting) MISR observational record provides an unprecedented data set with applications to multiple disciplines, documenting regional, global, short-term, and long-term changes in aerosol optical depths, aerosol type, near-surface particulate pollution, spectral top-of-atmosphere and surface albedos, aerosol plume-top and cloud-top heights, height-resolved cloud fractions, atmospheric motion vectors, and the structure of vegetated and ice-covered terrains. Recent computational advances include aerosol retrievals at finer spatial resolution than previously possible, and production of near-real time tropospheric winds with a latency of less than 3 hours, making possible for the first time the assimilation of MISR data into weather forecast models. In addition, recent algorithmic and technological developments provide the means of using and acquiring multi-angular data in new ways, such as the application of optical tomography to map 3-D atmospheric structure; building smaller multi-angle instruments in the future; and extending the multi-angular imaging methodology to the ultraviolet, shortwave infrared, and polarimetric realms. Such advances promise further enhancements to the observational power of the remote sensing approaches that MISR has pioneered.

Knowledge-based personalized search engine for the Web-based Human Musculoskeletal System Resources (HMSR) in biomechanics.

PubMed

Dao, Tien Tuan; Hoang, Tuan Nha; Ta, Xuan Hien; Tho, Marie Christine Ho Ba

2013-02-01

Human musculoskeletal system resources of the human body are valuable for the learning and medical purposes. Internet-based information from conventional search engines such as Google or Yahoo cannot response to the need of useful, accurate, reliable and good-quality human musculoskeletal resources related to medical processes, pathological knowledge and practical expertise. In this present work, an advanced knowledge-based personalized search engine was developed. Our search engine was based on a client-server multi-layer multi-agent architecture and the principle of semantic web services to acquire dynamically accurate and reliable HMSR information by a semantic processing and visualization approach. A security-enhanced mechanism was applied to protect the medical information. A multi-agent crawler was implemented to develop a content-based database of HMSR information. A new semantic-based PageRank score with related mathematical formulas were also defined and implemented. As the results, semantic web service descriptions were presented in OWL, WSDL and OWL-S formats. Operational scenarios with related web-based interfaces for personal computers and mobile devices were presented and analyzed. Functional comparison between our knowledge-based search engine, a conventional search engine and a semantic search engine showed the originality and the robustness of our knowledge-based personalized search engine. In fact, our knowledge-based personalized search engine allows different users such as orthopedic patient and experts or healthcare system managers or medical students to access remotely into useful, accurate, reliable and good-quality HMSR information for their learning and medical purposes. Copyright © 2012 Elsevier Inc. All rights reserved.

A remote instruction system empowered by tightly shared haptic sensation

NASA Astrophysics Data System (ADS)

Nishino, Hiroaki; Yamaguchi, Akira; Kagawa, Tsuneo; Utsumiya, Kouichi

2007-09-01

We present a system to realize an on-line instruction environment among physically separated participants based on a multi-modal communication strategy. In addition to visual and acoustic information, commonly used communication modalities in network environments, our system provides a haptic channel to intuitively conveying partners' sense of touch. The human touch sensation, however, is very sensitive for delays and jitters in the networked virtual reality (NVR) systems. Therefore, a method to compensate for such negative factors needs to be provided. We show an NVR architecture to implement a basic framework that can be shared by various applications and effectively deals with the problems. We take a hybrid approach to implement both data consistency by client-server and scalability by peer-to-peer models. As an application system built on the proposed architecture, a remote instruction system targeted at teaching handwritten characters and line patterns on a Korea-Japan high-speed research network also is mentioned.

Remote Sensing of Ecosystem Health: Opportunities, Challenges, and Future Perspectives

PubMed Central

Li, Zhaoqin; Xu, Dandan; Guo, Xulin

2014-01-01

Maintaining a healthy ecosystem is essential for maximizing sustainable ecological services of the best quality to human beings. Ecological and conservation research has provided a strong scientific background on identifying ecological health indicators and correspondingly making effective conservation plans. At the same time, ecologists have asserted a strong need for spatially explicit and temporally effective ecosystem health assessments based on remote sensing data. Currently, remote sensing of ecosystem health is only based on one ecosystem attribute: vigor, organization, or resilience. However, an effective ecosystem health assessment should be a comprehensive and dynamic measurement of the three attributes. This paper reviews opportunities of remote sensing, including optical, radar, and LiDAR, for directly estimating indicators of the three ecosystem attributes, discusses the main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system, and provides some future perspectives. The main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system are: (1) scale issue; (2) transportability issue; (3) data availability; and (4) uncertainties in health indicators estimated from remote sensing data. However, the Radarsat-2 constellation, upcoming new optical sensors on Worldview-3 and Sentinel-2 satellites, and improved technologies for the acquisition and processing of hyperspectral, multi-angle optical, radar, and LiDAR data and multi-sensoral data fusion may partly address the current challenges. PMID:25386759

Airborne remote sensing to detect greenbug stress to wheat

USDA-ARS?s Scientific Manuscript database

Vegetation indices calculated from the quantity of reflected electromagnetic radiation have been used to quantify levels of stress to plants. Greenbugs cause stress to wheat plants and therefore multi-spectral remote sensing may be useful for detecting greenbug infested wheat fields. The objective...

Design and Verification of Remote Sensing Image Data Center Storage Architecture Based on Hadoop

NASA Astrophysics Data System (ADS)

Tang, D.; Zhou, X.; Jing, Y.; Cong, W.; Li, C.

2018-04-01

The data center is a new concept of data processing and application proposed in recent years. It is a new method of processing technologies based on data, parallel computing, and compatibility with different hardware clusters. While optimizing the data storage management structure, it fully utilizes cluster resource computing nodes and improves the efficiency of data parallel application. This paper used mature Hadoop technology to build a large-scale distributed image management architecture for remote sensing imagery. Using MapReduce parallel processing technology, it called many computing nodes to process image storage blocks and pyramids in the background to improve the efficiency of image reading and application and sovled the need for concurrent multi-user high-speed access to remotely sensed data. It verified the rationality, reliability and superiority of the system design by testing the storage efficiency of different image data and multi-users and analyzing the distributed storage architecture to improve the application efficiency of remote sensing images through building an actual Hadoop service system.

Array processing for RFID tag localization exploiting multi-frequency signals

NASA Astrophysics Data System (ADS)

Zhang, Yimin; Li, Xin; Amin, Moeness G.

2009-05-01

RFID is an increasingly valuable business and technology tool for electronically identifying, locating, and tracking products, assets, and personnel. As a result, precise positioning and tracking of RFID tags and readers have received considerable attention from both academic and industrial communities. Finding the position of RFID tags is considered an important task in various real-time locating systems (RTLS). As such, numerous RFID localization products have been developed for various applications. The majority of RFID positioning systems is based on the fusion of pieces of relevant information, such as the range and the direction-of-arrival (DOA). For example, trilateration can determine the tag position by using the range information of the tag estimated from three or more spatially separated reader antennas. Triangulation is another method to locate RFID tags that use the direction-of-arrival (DOA) information estimated at multiple spatially separated locations. The RFID tag positions can also be determined through hybrid techniques that combine the range and DOA information. The focus of this paper to study the design and performance of the localization of passive RFID tags using array processing techniques in a multipath environment, and exploiting multi-frequency CW signals. The latter are used to decorrelate the coherent multipath signals for effective DOA estimation and for the purpose of accurate range estimation. Accordingly, the spatial and frequency dimensionalities are fully utilized for robust and accurate positioning of RFID tags.

Graph-based Data Modeling and Analysis for Data Fusion in Remote Sensing

NASA Astrophysics Data System (ADS)

Fan, Lei

Hyperspectral imaging provides the capability of increased sensitivity and discrimination over traditional imaging methods by combining standard digital imaging with spectroscopic methods. For each individual pixel in a hyperspectral image (HSI), a continuous spectrum is sampled as the spectral reflectance/radiance signature to facilitate identification of ground cover and surface material. The abundant spectrum knowledge allows all available information from the data to be mined. The superior qualities within hyperspectral imaging allow wide applications such as mineral exploration, agriculture monitoring, and ecological surveillance, etc. The processing of massive high-dimensional HSI datasets is a challenge since many data processing techniques have a computational complexity that grows exponentially with the dimension. Besides, a HSI dataset may contain a limited number of degrees of freedom due to the high correlations between data points and among the spectra. On the other hand, merely taking advantage of the sampled spectrum of individual HSI data point may produce inaccurate results due to the mixed nature of raw HSI data, such as mixed pixels, optical interferences and etc. Fusion strategies are widely adopted in data processing to achieve better performance, especially in the field of classification and clustering. There are mainly three types of fusion strategies, namely low-level data fusion, intermediate-level feature fusion, and high-level decision fusion. Low-level data fusion combines multi-source data that is expected to be complementary or cooperative. Intermediate-level feature fusion aims at selection and combination of features to remove redundant information. Decision level fusion exploits a set of classifiers to provide more accurate results. The fusion strategies have wide applications including HSI data processing. With the fast development of multiple remote sensing modalities, e.g. Very High Resolution (VHR) optical sensors, LiDAR, etc., fusion of multi-source data can in principal produce more detailed information than each single source. On the other hand, besides the abundant spectral information contained in HSI data, features such as texture and shape may be employed to represent data points from a spatial perspective. Furthermore, feature fusion also includes the strategy of removing redundant and noisy features in the dataset. One of the major problems in machine learning and pattern recognition is to develop appropriate representations for complex nonlinear data. In HSI processing, a particular data point is usually described as a vector with coordinates corresponding to the intensities measured in the spectral bands. This vector representation permits the application of linear and nonlinear transformations with linear algebra to find an alternative representation of the data. More generally, HSI is multi-dimensional in nature and the vector representation may lose the contextual correlations. Tensor representation provides a more sophisticated modeling technique and a higher-order generalization to linear subspace analysis. In graph theory, data points can be generalized as nodes with connectivities measured from the proximity of a local neighborhood. The graph-based framework efficiently characterizes the relationships among the data and allows for convenient mathematical manipulation in many applications, such as data clustering, feature extraction, feature selection and data alignment. In this thesis, graph-based approaches applied in the field of multi-source feature and data fusion in remote sensing area are explored. We will mainly investigate the fusion of spatial, spectral and LiDAR information with linear and multilinear algebra under graph-based framework for data clustering and classification problems.

Multi-image CAD employing features derived from ipsilateral mammographic views

NASA Astrophysics Data System (ADS)

Good, Walter F.; Zheng, Bin; Chang, Yuan-Hsiang; Wang, Xiao Hui; Maitz, Glenn S.; Gur, David

1999-05-01

On mammograms, certain kinds of features related to masses (e.g., location, texture, degree of spiculation, and integrated density difference) tend to be relatively invariant, or at last predictable, with respect to breast compression. Thus, ipsilateral pairs of mammograms may contain information not available from analyzing single views separately. To demonstrate the feasibility of incorporating multi-view features into CAD algorithm, `single-image' CAD was applied to each individual image in a set of 60 ipsilateral studies, after which all possible pairs of suspicious regions, consisting of one from each view, were formed. For these 402 pairs we defined and evaluated `multi-view' features such as: (1) relative position of centers of regions; (2) ratio of lengths of region projections parallel to nipple axis lines; (3) ratio of integrated contrast difference; (4) ratio of the sizes of the suspicious regions; and (5) measure of relative complexity of region boundaries. Each pair was identified as either a `true positive/true positive' (T) pair (i.e., two regions which are projections of the same actual mass), or as a falsely associated pair (F). Distributions for each feature were calculated. A Bayesian network was trained and tested to classify pairs of suspicious regions based exclusively on the multi-view features described above. Distributions for all features were significantly difference for T versus F pairs as indicated by likelihood ratios. Performance of the Bayesian network, which was measured by ROC analysis, indicates a significant ability to distinguish between T pairs and F pairs (Az equals 0.82 +/- 0.03), using information that is attributed to the multi-view content. This study is the first demonstration that there is a significant amount of spatial information that can be derived from ipsilateral pairs of mammograms.

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...

Multi-source remotely sensed data fusion for improving land cover classification

NASA Astrophysics Data System (ADS)

Chen, Bin; Huang, Bo; Xu, Bing

2017-02-01

Although many advances have been made in past decades, land cover classification of fine-resolution remotely sensed (RS) data integrating multiple temporal, angular, and spectral features remains limited, and the contribution of different RS features to land cover classification accuracy remains uncertain. We proposed to improve land cover classification accuracy by integrating multi-source RS features through data fusion. We further investigated the effect of different RS features on classification performance. The results of fusing Landsat-8 Operational Land Imager (OLI) data with Moderate Resolution Imaging Spectroradiometer (MODIS), China Environment 1A series (HJ-1A), and Advanced Spaceborne Thermal Emission and Reflection (ASTER) digital elevation model (DEM) data, showed that the fused data integrating temporal, spectral, angular, and topographic features achieved better land cover classification accuracy than the original RS data. Compared with the topographic feature, the temporal and angular features extracted from the fused data played more important roles in classification performance, especially those temporal features containing abundant vegetation growth information, which markedly increased the overall classification accuracy. In addition, the multispectral and hyperspectral fusion successfully discriminated detailed forest types. Our study provides a straightforward strategy for hierarchical land cover classification by making full use of available RS data. All of these methods and findings could be useful for land cover classification at both regional and global scales.

Multi-stage robust scheme for citrus identification from high resolution airborne images

NASA Astrophysics Data System (ADS)

Amorós-López, Julia; Izquierdo Verdiguier, Emma; Gómez-Chova, Luis; Muñoz-Marí, Jordi; Zoilo Rodríguez-Barreiro, Jorge; Camps-Valls, Gustavo; Calpe-Maravilla, Javier

2008-10-01

Identification of land cover types is one of the most critical activities in remote sensing. Nowadays, managing land resources by using remote sensing techniques is becoming a common procedure to speed up the process while reducing costs. However, data analysis procedures should satisfy the accuracy figures demanded by institutions and governments for further administrative actions. This paper presents a methodological scheme to update the citrus Geographical Information Systems (GIS) of the Comunidad Valenciana autonomous region, Spain). The proposed approach introduces a multi-stage automatic scheme to reduce visual photointerpretation and ground validation tasks. First, an object-oriented feature extraction process is carried out for each cadastral parcel from very high spatial resolution (VHR) images (0.5m) acquired in the visible and near infrared. Next, several automatic classifiers (decision trees, multilayer perceptron, and support vector machines) are trained and combined to improve the final accuracy of the results. The proposed strategy fulfills the high accuracy demanded by policy makers by means of combining automatic classification methods with visual photointerpretation available resources. A level of confidence based on the agreement between classifiers allows us an effective management by fixing the quantity of parcels to be reviewed. The proposed methodology can be applied to similar problems and applications.

Storage and retrieval of digital images in dermatology.

PubMed

Bittorf, A; Krejci-Papa, N C; Diepgen, T L

1995-11-01

Differential diagnosis in dermatology relies on the interpretation of visual information in the form of clinical and histopathological images. Up until now, reference images have had to be retrieved from textbooks and/or appropriate journals. To overcome inherent limitations of those storage media with respect to the number of images stored, display, and search parameters available, we designed a computer-based database of digitized dermatologic images. Images were taken from the photo archive of the Dermatological Clinic of the University of Erlangen. A database was designed using the Entity-Relationship approach. It was implemented on a PC-Windows platform using MS Access* and MS Visual Basic®. As WWW-server a Sparc 10 workstation was used with the CERN Hypertext-Transfer-Protocol-Daemon (httpd) 3.0 pre 6 software running. For compressed storage on a hard drive, a quality factor of 60 allowed on-screen differential diagnosis and corresponded to a compression factor of 1:35 for clinical images and 1:40 for histopathological images. Hierarchical keys of clinical or histopathological criteria permitted multi-criteria searches. A script using the Common Gateway Interface (CGI) enabled remote search and image retrieval via the World-Wide-Web (W3). A dermatologic image database, featurig clinical and histopathological images was constructed which allows for multi-parameter searches and world-wide remote access.

Diverse Planning for UAV Control and Remote Sensing

PubMed Central

Tožička, Jan; Komenda, Antonín

2016-01-01

Unmanned aerial vehicles (UAVs) are suited to various remote sensing missions, such as measuring air quality. The conventional method of UAV control is by human operators. Such an approach is limited by the ability of cooperation among the operators controlling larger fleets of UAVs in a shared area. The remedy for this is to increase autonomy of the UAVs in planning their trajectories by considering other UAVs and their plans. To provide such improvement in autonomy, we need better algorithms for generating alternative trajectory variants that the UAV coordination algorithms can utilize. In this article, we define a novel family of multi-UAV sensing problems, solving task allocation of huge number of tasks (tens of thousands) to a group of configurable UAVs with non-zero weight of equipped sensors (comprising the air quality measurement as well) together with two base-line solvers. To solve the problem efficiently, we use an algorithm for diverse trajectory generation and integrate it with a solver for the multi-UAV coordination problem. Finally, we experimentally evaluate the multi-UAV sensing problem solver. The evaluation is done on synthetic and real-world-inspired benchmarks in a multi-UAV simulator. Results show that diverse planning is a valuable method for remote sensing applications containing multiple UAVs. PMID:28009831

Diverse Planning for UAV Control and Remote Sensing.

PubMed

Tožička, Jan; Komenda, Antonín

2016-12-21

Unmanned aerial vehicles (UAVs) are suited to various remote sensing missions, such as measuring air quality. The conventional method of UAV control is by human operators. Such an approach is limited by the ability of cooperation among the operators controlling larger fleets of UAVs in a shared area. The remedy for this is to increase autonomy of the UAVs in planning their trajectories by considering other UAVs and their plans. To provide such improvement in autonomy, we need better algorithms for generating alternative trajectory variants that the UAV coordination algorithms can utilize. In this article, we define a novel family of multi-UAV sensing problems, solving task allocation of huge number of tasks (tens of thousands) to a group of configurable UAVs with non-zero weight of equipped sensors (comprising the air quality measurement as well) together with two base-line solvers. To solve the problem efficiently, we use an algorithm for diverse trajectory generation and integrate it with a solver for the multi-UAV coordination problem. Finally, we experimentally evaluate the multi-UAV sensing problem solver. The evaluation is done on synthetic and real-world-inspired benchmarks in a multi-UAV simulator. Results show that diverse planning is a valuable method for remote sensing applications containing multiple UAVs.

Multi-class geospatial object detection and geographic image classification based on collection of part detectors

NASA Astrophysics Data System (ADS)

Cheng, Gong; Han, Junwei; Zhou, Peicheng; Guo, Lei

2014-12-01

The rapid development of remote sensing technology has facilitated us the acquisition of remote sensing images with higher and higher spatial resolution, but how to automatically understand the image contents is still a big challenge. In this paper, we develop a practical and rotation-invariant framework for multi-class geospatial object detection and geographic image classification based on collection of part detectors (COPD). The COPD is composed of a set of representative and discriminative part detectors, where each part detector is a linear support vector machine (SVM) classifier used for the detection of objects or recurring spatial patterns within a certain range of orientation. Specifically, when performing multi-class geospatial object detection, we learn a set of seed-based part detectors where each part detector corresponds to a particular viewpoint of an object class, so the collection of them provides a solution for rotation-invariant detection of multi-class objects. When performing geographic image classification, we utilize a large number of pre-trained part detectors to discovery distinctive visual parts from images and use them as attributes to represent the images. Comprehensive evaluations on two remote sensing image databases and comparisons with some state-of-the-art approaches demonstrate the effectiveness and superiority of the developed framework.

Monitoring cover crops using radar remote sensing in southern Ontario, Canada

NASA Astrophysics Data System (ADS)

Shang, J.; Huang, X.; Liu, J.; Wang, J.

2016-12-01

Information on agricultural land surface conditions is important for developing best land management practices to maintain the overall health of the fields. The climate condition supports one harvest per year for the majority of the field crops in Canada, with a relative short growing season between May and September. During the non-growing-season months (October to the following April), many fields are traditionally left bare. In more recent year, there has been an increased interest in planting cover crops. Benefits of cover crops include boosting soil organic matters, preventing soil from erosion, retaining soil moisture, and reducing surface runoff hence protecting water quality. Optical remote sensing technology has been exploited for monitoring cover crops. However limitations inherent to optical sensors such as cloud interference and signal saturation (when leaf area index is above 2.5) impeded its operational application. Radar remote sensing on the other hand is not hindered by unfavorable weather conditions, and the signal continues to be sensitive to crop growth beyond the saturation point of optical sensors. It offers a viable means for capturing timely information on field surface conditions (with or without crop cover) or crop development status. This research investigated the potential of using multi-temporal RADARSAT-2 C-band synthetic aperture radar (SAR) data collected in 2015 over multiple fields of winter wheat, corn and soybean crops in southern Ontario, Canada, to retrieve information on the presence of cover crops and their growth status. Encouraging results have been obtained. This presentation will report the methodology developed and the results obtained.

Multi-angle Imaging Spectro Radiometer (MISR) Design Issues Influened by Performance Requirements

NASA Technical Reports Server (NTRS)

Bruegge, C. J.; White, M. L.; Chrien, N. C. L.; Villegas, E. B.; Raouf, N.

1993-01-01

The design of an Earth Remote Sensing Sensor, such as the Multi-angle Imaging SpectroRadiometer (MISR), begins with a set of science requirements and is quickly followed by a set of instrument specifications.

Airborne Digital Sensor System and GPS-aided inertial technology for direct geopositioning in rough terrain

USGS Publications Warehouse

Sanchez, Richard D.

2004-01-01

High-resolution airborne digital cameras with onboard data collection based on the Global Positioning System (GPS) and inertial navigation systems (INS) technology may offer a real-time means to gather accurate topographic map information by reducing ground control and eliminating aerial triangulation. Past evaluations of this integrated system over relatively flat terrain have proven successful. The author uses Emerge Digital Sensor System (DSS) combined with Applanix Corporation?s Position and Orientation Solutions for Direct Georeferencing to examine the positional mapping accuracy in rough terrain. The positional accuracy documented in this study did not meet large-scale mapping requirements owing to an apparent system mechanical failure. Nonetheless, the findings yield important information on a new approach for mapping in Antarctica and other remote or inaccessible areas of the world.

Earth Observation Research for GMES Initial Operations

NASA Astrophysics Data System (ADS)

van Beijma, Sybrand; Balzter, Heiko; Nicolas-Perea, Virginia

2013-04-01

GMES Initial Operations - Network for Earth Observation Research Training (GIONET) is a Marie Curie funded project that aims to establish the first of a kind European Centre of Excellence for Earth Observation Research Training. GIONET is a partnership of leading Universities, research institutes and private companies from across Europe aiming to cultivate a community of early stage researchers in the areas of optical and radar remote sensing skilled for the emerging GMES land monitoring services during the GMES Initial Operations period (2011-2013) and beyond. GIONET is expected to satisfy the demand for highly skilled researchers and provide personnel for operational phase of the GMES and monitoring and emergency services. It will achieve this by: * Providing postgraduate training in Earth Observation Science that exposes students to different research disciplines and complementary skills, providing work experiences in the private and academic sectors, and leading to a recognized qualification (Doctorate). * Enabling access to first class training in both fundamental and applied research skills to early-stage researchers at world-class academic centres and market leaders in the private sector. * Building on the experience from previous GMES research and development projects in the land monitoring and emergency information services. * Developing a collaborative training network, through the placement of researchers for short periods in other GIONET organizations. Reliable, thorough and up-to-date environmental information is essential for understanding climate change the impacts it has on people's lives and ways to adapt to them. The GIONET researchers are being trained to understand the complex physical processes that determine how electromagnetic radiation interacts with the atmosphere and the land surface ultimately form the signal received by a satellite. In order to achieve this, the researchers have been placed in industry and universities across Europe, as well as receiving the best technical training and scientific education. This training is currently being delivered through individually supervised research, international summer schools and local training. GIONET will develop better methods for monitoring climate change, environmental disasters and land cover change. It will also lead to the development of new methods using satellite monitoring for disaster relief after landslides and floods, controlling deforestation and overseeing the protection of tropical rainforests, as well as for climate change monitoring, lake water quality measurement and coastal erosion assessment. The training program through supervised research focuses on 14 research topics (each carried out by an Early Stage Researchers based in one of the partner organization) divided in 5 main areas: * Forest monitoring: o Global biomass information systems o Forest monitoring of the Congo Basin using Synthetic Aperture Radar (SAR) o Multi-concept Earth Observation capabilities for biomass mapping and change detection: synergy of multi-temporal and multi-frequency interferometric radar and optical satellite data * Land cover and change: o Multi-scale remote sensing synergy for land process studies: from field spectrometry to airborne hyperspectral and LiDAR campaigns to radar-optical satellite data o Multi-temporal, multi-frequency SAR for landscape dynamics * Coastal zone and freshwater monitoring: o SAR-based Earth Observation in support of management of intertidal salt marsh habitats o Dynamics and conservation ecology of emergent and submerged macrophytes in Lake Balaton using airborne remote sensing o Satellite remote sensing of water quality (chlorophyll and suspended sediment) using MODIS and ship-mounted LIDAR * Geohazards and emergency response: o Methods for detection and monitoring of small scale land surface feature changes in complex crisis situations o Monitoring landslide displacements with Radar Interferometry o DINSAR/PSI hybrid methodologies for ground-motion monitoring * Climate adaptation and emergency response: o Earth Observation based analysis of regional impact of climate change induced water stress patterns fuelling human crisis and conflict situations in semi dry climate regimes o Satellite derived information for drought detection and estimation of the water balance GIONET will also cover methodologies including (i) modelling fundamental radiative processes determining the satellite signal, (ii) atmospheric correction and calibration, (iii) processing higher-order data products, (iii) developing information products from satellite data to meet user requirements, and (iv) statistical methods for assessing the quality and accuracy of data products. These methodologies will enable the researchers to develop careers in the evolving GMES (renamed to Copernicus) Services, network with the GMES community and contribute to rolling out the GMES Program. Communication skills and effective engagement with stakeholders and the public will form an integral part of the training. The Earth Observation methods developed in GIONET will benefit the economy in Europe.

Cooperative Localization for Multi-AUVs Based on GM-PHD Filters and Information Entropy Theory

PubMed Central

Zhang, Lichuan; Wang, Tonghao; Xu, Demin

2017-01-01

Cooperative localization (CL) is considered a promising method for underwater localization with respect to multiple autonomous underwater vehicles (multi-AUVs). In this paper, we proposed a CL algorithm based on information entropy theory and the probability hypothesis density (PHD) filter, aiming to enhance the global localization accuracy of the follower. In the proposed framework, the follower carries lower cost navigation systems, whereas the leaders carry better ones. Meanwhile, the leaders acquire the followers’ observations, including both measurements and clutter. Then, the PHD filters are utilized on the leaders and the results are communicated to the followers. The followers then perform weighted summation based on all received messages and obtain a final positioning result. Based on the information entropy theory and the PHD filter, the follower is able to acquire a precise knowledge of its position. PMID:28991191

Urban area thermal monitoring: Liepaja case study using satellite and aerial thermal data

NASA Astrophysics Data System (ADS)

Gulbe, Linda; Caune, Vairis; Korats, Gundars

2017-12-01

The aim of this study is to explore large (60 m/pixel) and small scale (individual building level) temperature distribution patterns from thermal remote sensing data and to conclude what kind of information could be extracted from thermal remote sensing on regular basis. Landsat program provides frequent large scale thermal images useful for analysis of city temperature patterns. During the study correlation between temperature patterns and vegetation content based on NDVI and building coverage based on OpenStreetMap data was studied. Landsat based temperature patterns were independent from the season, negatively correlated with vegetation content and positively correlated with building coverage. Small scale analysis included spatial and raster descriptor analysis for polygons corresponding to roofs of individual buildings for evaluating insulation of roofs. Remote sensing and spatial descriptors are poorly related to heat consumption data, however, thermal aerial data median and entropy can help to identify poorly insulated roofs. Automated quantitative roof analysis has high potential for acquiring city wide information about roof insulation, but quality is limited by reference data quality and information on building types, and roof materials would be crucial for further studies.

Quantifying cyanobacterial phycocyanin concentration in turbid productive waters: a quasi-analytical approach

USDA-ARS?s Scientific Manuscript database

In this research, we present a novel technique to monitor cyanobacterial algal bloom using remote sensing measurements. We have used a multi-band quasi analytical algorithm that determines phytoplankton absorption coefficients, aF('), from above-surface remote sensing reflectance, Rrs('). In situ da...

Novel Hyperspectral Sun Photometer for Satellite Remote Sensing Data Radiometeic Calibration and Atmospheric Aerosol Studies

NASA Technical Reports Server (NTRS)

Pagnutti, Mary; Ryan, Robert E.; Holekamp, Kara; Harrington, Gary; Frisbie, Troy

2006-01-01

A simple and cost-effective, hyperspectral sun photometer for radiometric vicarious remote sensing system calibration, air quality monitoring, and potentially in-situ planetary climatological studies, was developed. The device was constructed solely from off the shelf components and was designed to be easily deployable for support of short-term verification and validation data collects. This sun photometer not only provides the same data products as existing multi-band sun photometers but also the potential of hyperspectral optical depth and diffuse-to-global products. As compared to traditional sun photometers, this device requires a simpler setup, less data acquisition time and allows for a more direct calibration approach. Fielding this instrument has also enabled Stennis Space Center (SSC) Applied Sciences Directorate personnel to cross-calibrate existing sun photometers. This innovative research will position SSC personnel to perform air quality assessments in support of the NASA Applied Sciences Program's National Applications program element as well as to develop techniques to evaluate aerosols in a Martian or other planetary atmosphere.

Land cover's refined classification based on multi source of remote sensing information fusion: a case study of national geographic conditions census in China

NASA Astrophysics Data System (ADS)

Cheng, Tao; Zhang, Jialong; Zheng, Xinyan; Yuan, Rujin

2018-03-01

The project of The First National Geographic Conditions Census developed by Chinese government has designed the data acquisition content and indexes, and has built corresponding classification system mainly based on the natural property of material. However, the unified standard for land cover classification system has not been formed; the production always needs converting to meet the actual needs. Therefore, it proposed a refined classification method based on multi source of remote sensing information fusion. It takes the third-level classes of forest land and grassland for example, and has collected the thematic data of Vegetation Map of China (1:1,000,000), attempts to develop refined classification utilizing raster spatial analysis model. Study area is selected, and refined classification is achieved by using the proposed method. The results show that land cover within study area is divided principally among 20 classes, from subtropical broad-leaved forest (31131) to grass-forb community type of low coverage grassland (41192); what's more, after 30 years in the study area, climatic factors, developmental rhythm characteristics and vegetation ecological geographical characteristics have not changed fundamentally, only part of the original vegetation types have changed in spatial distribution range or land cover types. Research shows that refined classification for the third-level classes of forest land and grassland could make the results take on both the natural attributes of the original and plant community ecology characteristics, which could meet the needs of some industry application, and has certain practical significance for promoting the product of The First National Geographic Conditions Census.

The Retrieval of Aerosol Optical Thickness Using the MERIS Instrument

NASA Astrophysics Data System (ADS)

Mei, L.; Rozanov, V. V.; Vountas, M.; Burrows, J. P.; Levy, R. C.; Lotz, W.

2015-12-01

Retrieval of aerosol properties for satellite instruments without shortwave-IR spectral information, multi-viewing, polarization and/or high-temporal observation ability is a challenging problem for spaceborne aerosol remote sensing. However, space based instruments like the MEdium Resolution Imaging Spectrometer (MERIS) and the successor, Ocean and Land Colour Instrument (OLCI) with high calibration accuracy and high spatial resolution provide unique abilities for obtaining valuable aerosol information for a better understanding of the impact of aerosols on climate, which is still one of the largest uncertainties of global climate change evaluation. In this study, a new Aerosol Optical Thickness (AOT) retrieval algorithm (XBAER: eXtensible Bremen AErosol Retrieval) is presented. XBAER utilizes the global surface spectral library database for the determination of surface properties while the MODIS collection 6 aerosol type treatment is adapted for the aerosol type selection. In order to take the surface Bidirectional Reflectance Distribution Function (BRDF) effect into account for the MERIS reduce resolution (1km) retrieval, a modified Ross-Li mode is used. The AOT is determined in the algorithm using lookup tables including polarization created using Radiative Transfer Model SCIATRAN3.4, by minimizing the difference between atmospheric corrected surface reflectance with given AOT and the surface reflectance calculated from the spectral library. The global comparison with operational MODIS C6 product, Multi-angle Imaging SpectroRadiometer (MISR) product, Advanced Along-Track Scanning Radiometer (AATSR) aerosol product and the validation using AErosol RObotic NETwork (AERONET) show promising results. The current XBAER algorithm is only valid for aerosol remote sensing over land and a similar method will be extended to ocean later.

Spatial distribution and ecological environment analysis of great gerbil in Xinjiang Plague epidemic foci based on remote sensing

NASA Astrophysics Data System (ADS)

Gao, Mengxu; Li, Qun; Cao, Chunxiang; Wang, Juanle

2014-03-01

Yersinia pestis (Plague bacterium) from great gerbil was isolated in 2005 in Xinjiang Dzungarian Basin, which confirmed the presence of the plague epidemic foci. This study analysed the spatial distribution and suitable habitat of great gerbil based on the monitoring data of great gerbil from Chinese Center for Disease Control and Prevention, as well as the ecological environment elements obtained from remote sensing products. The results showed that: (1) 88.5% (277/313) of great gerbil distributed in the area of elevation between 200 and 600 meters. (2) All the positive points located in the area with a slope of 0-3 degree, and the sunny tendency on aspect was not obvious. (3) All 313 positive points of great gerbil distributed in the area with an average annual temperature from 5 to 11 °C, and 165 points with an average annual temperature from 7 to 9 °C. (4) 72.8% (228/313) of great gerbil survived in the area with an annual precipitation of 120-200mm. (5) The positive points of great gerbil increased correspondingly with the increasing of NDVI value, but there is no positive point when NDVI is higher than 0.521, indicating the suitability of vegetation for great gerbil. This study explored a broad and important application for the monitoring and prevention of plague using remote sensing and geographic information system.

An empirical study on the utility of BRDF model parameters and topographic parameters for mapping vegetation in a semi-arid region with MISR imagery

USDA-ARS?s Scientific Manuscript database

Multi-angle remote sensing has been proved useful for mapping vegetation community types in desert regions. Based on Multi-angle Imaging Spectro-Radiometer (MISR) multi-angular images, this study compares roles played by Bidirectional Reflectance Distribution Function (BRDF) model parameters with th...

A research on the positioning technology of vehicle navigation system from single source to "ASPN"

NASA Astrophysics Data System (ADS)

Zhang, Jing; Li, Haizhou; Chen, Yu; Chen, Hongyue; Sun, Qian

2017-10-01

Due to the suddenness and complexity of modern warfare, land-based weapon systems need to have precision strike capability on roads and railways. The vehicle navigation system is one of the most important equipments for the land-based weapon systems that have precision strick capability. There are inherent shortcomings for single source navigation systems to provide continuous and stable navigation information. To overcome the shortcomings, the multi-source positioning technology is developed. The All Source Positioning and Navigaiton (ASPN) program was proposed in 2010, which seeks to enable low cost, robust, and seamless navigation solutions for military to use on any operational platform and in any environment with or without GPS. The development trend of vehicle positioning technology was reviewed in this paper. The trend indicates that the positioning technology is developed from single source and multi-source to ASPN. The data fusion techniques based on multi-source and ASPN was analyzed in detail.

Fifty years dynamics of Russian forests: Impacts on the earth system

NASA Astrophysics Data System (ADS)

Shvidenko, Anatoly; Schepaschenko, Dmitry; Kraxner, Florian

2015-04-01

The paper presents a succinct history of Russian forests during the time period of 1960-2010 and reanalysis of their impacts on global carbon and nitrogen cycles. We present dynamics of land cover change (including major categories of forest land) and biometric characteristics of forests (species composition, age structure, growing stock volume etc.) based on reconciling all relevant information (data of forest and land inventories, official forest management statistics, multi-sensor remote sensing products, data of forest pathological monitoring etc.). Completeness and reliability of background information was different during the period of the study. Forest inventory data and official statistics were partially modified based on relevant auxiliary information and used for 1960-2000. The analysis for 2001-2010 was provided with a crucial use of multi-sensor remote sensing data. For this last period a hybrid forest mask was developed at resolution of 230m by integration of 8 remote sensing products and using geographical weighted regression and data of crowdsourcing. During the considered 50 years forested areas of Russia substantially increased by middle of 1990s and slightly declined (at about 5%) after. Indicators needed for assessment of carbon and nitrogen cycles of forest ecosystems were defined for the entire period (aggregated estimates by decades for 1960-2000 and yearly for 2001-2010) based on unified methodology with some peculiarities following from availability of information. Major results were obtained by landscape-ecosystem method that uses as comprehensive as possible empirical and semi-empirical information on ecosystems and landscapes in form of an Integrated Land Information System and complimentary combines pool- and flux-based methods. We discuss and quantify major drivers of forest cover change (socio-economic, environmental and climatic) including forest management (harvest, reforestation and afforestation), impacts of seasonal weather on carbon fluxes (Net Primary Production, Heterotrophic Respiration), disturbances (fire, outbreaks of insects and diseases), and industrial pressure (land change, air pollution, water and soil contamination). During the entire period Russian forests provided the net carbon sink in range from 350-700 Tg C yr-1 with inter-annual variability in limits of 10-15% for the entire country. The overall sink is a result of superposition of trends of major carbon fluxes (caused by removal of harvested wood and use of forest products; land cover change; impact of climatic trends; change of disturbance regimes) and inter-annual variation of seasonal weather. Major indicators of the nitrogen cycle are assessed and discussed in connection with the carbon cycle. We provide comparative analysis of other results published for the considered period taken into account successive improvements of information and methodology used for studying the major biogeochemical cycles.

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.

Tele-autonomous systems: New methods for projecting and coordinating intelligent action at a distance

NASA Technical Reports Server (NTRS)

Conway, Lynn; Volz, Richard; Walker, Michael W.

1989-01-01

There is a growing need for humans to perform complex remote operations and to extend the intelligence and experience of experts to distant applications. It is asserted that a blending of human intelligence, modern information technology, remote control, and intelligent autonomous systems is required, and have coined the term tele-autonomous technology, or tele-automation, for methods producing intelligent action at a distance. Tele-automation goes beyond autonomous control by blending in human intelligence. It goes beyond tele-operation by incorporating as much autonomy as possible and/or reasonable. A new approach is discussed for solving one of the fundamental problems facing tele-autonomous systems: The need to overcome time delays due to telemetry and signal propagation. New concepts are introduced called time and position clutches, that allow the time and position frames between the local user control and the remote device being controlled, to be desynchronized respectively. The design and implementation of these mechanisms are described in detail. It is demonstrated that these mechanisms lead to substantial telemanipulation performance improvements, including the result of improvements even in the absence of time delays. The new controls also yield a simple protocol for control handoffs of manipulation tasks between local operators and remote systems.

Research on active imaging information transmission technology of satellite borne quantum remote sensing

NASA Astrophysics Data System (ADS)

Bi, Siwen; Zhen, Ming; Yang, Song; Lin, Xuling; Wu, Zhiqiang

2017-08-01

According to the development and application needs of Remote Sensing Science and technology, Prof. Siwen Bi proposed quantum remote sensing. Firstly, the paper gives a brief introduction of the background of quantum remote sensing, the research status and related researches at home and abroad on the theory, information mechanism and imaging experiments of quantum remote sensing and the production of principle prototype.Then, the quantization of pure remote sensing radiation field, the state function and squeezing effect of quantum remote sensing radiation field are emphasized. It also describes the squeezing optical operator of quantum light field in active imaging information transmission experiment and imaging experiments, achieving 2-3 times higher resolution than that of coherent light detection imaging and completing the production of quantum remote sensing imaging prototype. The application of quantum remote sensing technology can significantly improve both the signal-to-noise ratio of information transmission imaging and the spatial resolution of quantum remote sensing .On the above basis, Prof.Bi proposed the technical solution of active imaging information transmission technology of satellite borne quantum remote sensing, launched researches on its system composition and operation principle and on quantum noiseless amplifying devices, providing solutions and technical basis for implementing active imaging information technology of satellite borne Quantum Remote Sensing.

Multiple kernel SVR based on the MRE for remote sensing water depth fusion detection

NASA Astrophysics Data System (ADS)

Wang, Jinjin; Ma, Yi; Zhang, Jingyu

2018-03-01

Remote sensing has an important means of water depth detection in coastal shallow waters and reefs. Support vector regression (SVR) is a machine learning method which is widely used in data regression. In this paper, SVR is used to remote sensing multispectral bathymetry. Aiming at the problem that the single-kernel SVR method has a large error in shallow water depth inversion, the mean relative error (MRE) of different water depth is retrieved as a decision fusion factor with single kernel SVR method, a multi kernel SVR fusion method based on the MRE is put forward. And taking the North Island of the Xisha Islands in China as an experimentation area, the comparison experiments with the single kernel SVR method and the traditional multi-bands bathymetric method are carried out. The results show that: 1) In range of 0 to 25 meters, the mean absolute error(MAE)of the multi kernel SVR fusion method is 1.5m,the MRE is 13.2%; 2) Compared to the 4 single kernel SVR method, the MRE of the fusion method reduced 1.2% (1.9%) 3.4% (1.8%), and compared to traditional multi-bands method, the MRE reduced 1.9%; 3) In 0-5m depth section, compared to the single kernel method and the multi-bands method, the MRE of fusion method reduced 13.5% to 44.4%, and the distribution of points is more concentrated relative to y=x.

The multi-parameter remote measurement of rainfall

NASA Technical Reports Server (NTRS)

Atlas, D.; Ulbrich, C. W.; Meneghini, R.

1982-01-01

The measurement of rainfall by remote sensors is investigated. One parameter radar rainfall measurement is limited because both reflectivity and rain rate are dependent on at least two parameters of the drop size distribution (DSD), i.e., representative raindrop size and number concentration. A generalized rain parameter diagram is developed which includes a third distribution parameter, the breadth of the DSD, to better specify rain rate and all possible remote variables. Simulations show the improvement in accuracy attainable through the use of combinations of two and three remote measurables. The spectrum of remote measurables is reviewed. These include path integrated techniques of radiometry and of microwave and optical attenuation.

Application of remote sensing to water resources problems

NASA Technical Reports Server (NTRS)

Clapp, J. L.

1972-01-01

The following conclusions were reached concerning the applications of remote sensing to water resources problems: (1) Remote sensing methods provide the most practical method of obtaining data for many water resources problems; (2) the multi-disciplinary approach is essential to the effective application of remote sensing to water resource problems; (3) there is a correlation between the amount of suspended solids in an effluent discharged into a water body and reflected energy; (4) remote sensing provides for more effective and accurate monitoring, discovery and characterization of the mixing zone of effluent discharged into a receiving water body; and (5) it is possible to differentiate between blue and blue-green algae.

Mapping Migratory Bird Prevalence Using Remote Sensing Data Fusion

PubMed Central

Swatantran, Anu; Dubayah, Ralph; Goetz, Scott; Hofton, Michelle; Betts, Matthew G.; Sun, Mindy; Simard, Marc; Holmes, Richard

2012-01-01

Background Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA. Methodology and Principal Findings A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy (“fusion”) models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species. Conclusion and Significance Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level. PMID:22235254

Uniform competency-based local feature extraction for remote sensing images

NASA Astrophysics Data System (ADS)

Sedaghat, Amin; Mohammadi, Nazila

2018-01-01

Local feature detectors are widely used in many photogrammetry and remote sensing applications. The quantity and distribution of the local features play a critical role in the quality of the image matching process, particularly for multi-sensor high resolution remote sensing image registration. However, conventional local feature detectors cannot extract desirable matched features either in terms of the number of correct matches or the spatial and scale distribution in multi-sensor remote sensing images. To address this problem, this paper proposes a novel method for uniform and robust local feature extraction for remote sensing images, which is based on a novel competency criterion and scale and location distribution constraints. The proposed method, called uniform competency (UC) local feature extraction, can be easily applied to any local feature detector for various kinds of applications. The proposed competency criterion is based on a weighted ranking process using three quality measures, including robustness, spatial saliency and scale parameters, which is performed in a multi-layer gridding schema. For evaluation, five state-of-the-art local feature detector approaches, namely, scale-invariant feature transform (SIFT), speeded up robust features (SURF), scale-invariant feature operator (SFOP), maximally stable extremal region (MSER) and hessian-affine, are used. The proposed UC-based feature extraction algorithms were successfully applied to match various synthetic and real satellite image pairs, and the results demonstrate its capability to increase matching performance and to improve the spatial distribution. The code to carry out the UC feature extraction is available from href="https://www.researchgate.net/publication/317956777_UC-Feature_Extraction.

Mapping migratory bird prevalence using remote sensing data fusion.

PubMed

Swatantran, Anu; Dubayah, Ralph; Goetz, Scott; Hofton, Michelle; Betts, Matthew G; Sun, Mindy; Simard, Marc; Holmes, Richard

2012-01-01

Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA. A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy ("fusion") models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species. Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level.

Geometric registration of remotely sensed data with SAMIR

NASA Astrophysics Data System (ADS)

Gianinetto, Marco; Barazzetti, Luigi; Dini, Luigi; Fusiello, Andrea; Toldo, Roberto

2015-06-01

The commercial market offers several software packages for the registration of remotely sensed data through standard one-to-one image matching. Although very rapid and simple, this strategy does not take into consideration all the interconnections among the images of a multi-temporal data set. This paper presents a new scientific software, called Satellite Automatic Multi-Image Registration (SAMIR), able to extend the traditional registration approach towards multi-image global processing. Tests carried out with high-resolution optical (IKONOS) and high-resolution radar (COSMO-SkyMed) data showed that SAMIR can improve the registration phase with a more rigorous and robust workflow without initial approximations, user's interaction or limitation in spatial/spectral data size. The validation highlighted a sub-pixel accuracy in image co-registration for the considered imaging technologies, including optical and radar imagery.

Remote Versus Local Forcing of Chlorophyll Variability in the South Atlantic Bight

NASA Technical Reports Server (NTRS)

Signorini, Sergio R.; McClain, Charles R.

2006-01-01

This TM documents results of analyses addressing the local versus remote forcing of chlorophyll variability on the shelf and slope regions of the South Atlantic Bight (SAB) based on satellite-derived products and a limited amount of in situ data. This study is part of a larger multi-disciplinary, multi-institutional effort to study the Eastern U.S. Continental Shelf carbon budget (U.S. Eastern Continental Shelf Carbon Budget: Modeling, Data Assimilation, and Analysis, U.S. ECoS), a project funded by the NASA Earth System Enterprise Interdisciplinary Science Program that started in the summer of 2004.

Automated road network extraction from high spatial resolution multi-spectral imagery

NASA Astrophysics Data System (ADS)

Zhang, Qiaoping

For the last three decades, the Geomatics Engineering and Computer Science communities have considered automated road network extraction from remotely-sensed imagery to be a challenging and important research topic. The main objective of this research is to investigate the theory and methodology of automated feature extraction for image-based road database creation, refinement or updating, and to develop a series of algorithms for road network extraction from high resolution multi-spectral imagery. The proposed framework for road network extraction from multi-spectral imagery begins with an image segmentation using the k-means algorithm. This step mainly concerns the exploitation of the spectral information for feature extraction. The road cluster is automatically identified using a fuzzy classifier based on a set of predefined road surface membership functions. These membership functions are established based on the general spectral signature of road pavement materials and the corresponding normalized digital numbers on each multi-spectral band. Shape descriptors of the Angular Texture Signature are defined and used to reduce the misclassifications between roads and other spectrally similar objects (e.g., crop fields, parking lots, and buildings). An iterative and localized Radon transform is developed for the extraction of road centerlines from the classified images. The purpose of the transform is to accurately and completely detect the road centerlines. It is able to find short, long, and even curvilinear lines. The input image is partitioned into a set of subset images called road component images. An iterative Radon transform is locally applied to each road component image. At each iteration, road centerline segments are detected based on an accurate estimation of the line parameters and line widths. Three localization approaches are implemented and compared using qualitative and quantitative methods. Finally, the road centerline segments are grouped into a road network. The extracted road network is evaluated against a reference dataset using a line segment matching algorithm. The entire process is unsupervised and fully automated. Based on extensive experimentation on a variety of remotely-sensed multi-spectral images, the proposed methodology achieves a moderate success in automating road network extraction from high spatial resolution multi-spectral imagery.

Incorporating Applied Undergraduate Research in Senior to Graduate Level Remote Sensing Courses

ERIC Educational Resources Information Center

Henley, Richard B.; Unger, Daniel R.; Kulhavy, David L.; Hung, I-Kuai

2016-01-01

An Arthur Temple College of Forestry and Agriculture (ATCOFA) senior spatial science undergraduate student engaged in a multi-course undergraduate research project to expand his expertise in remote sensing and assess the applied instruction methodology employed within ATCOFA. The project consisted of performing a change detection…

UAV-based urban structural damage assessment using object-based image analysis and semantic reasoning

NASA Astrophysics Data System (ADS)

Fernandez Galarreta, J.; Kerle, N.; Gerke, M.

2015-06-01

Structural damage assessment is critical after disasters but remains a challenge. Many studies have explored the potential of remote sensing data, but limitations of vertical data persist. Oblique imagery has been identified as more useful, though the multi-angle imagery also adds a new dimension of complexity. This paper addresses damage assessment based on multi-perspective, overlapping, very high resolution oblique images obtained with unmanned aerial vehicles (UAVs). 3-D point-cloud assessment for the entire building is combined with detailed object-based image analysis (OBIA) of façades and roofs. This research focuses not on automatic damage assessment, but on creating a methodology that supports the often ambiguous classification of intermediate damage levels, aiming at producing comprehensive per-building damage scores. We identify completely damaged structures in the 3-D point cloud, and for all other cases provide the OBIA-based damage indicators to be used as auxiliary information by damage analysts. The results demonstrate the usability of the 3-D point-cloud data to identify major damage features. Also the UAV-derived and OBIA-processed oblique images are shown to be a suitable basis for the identification of detailed damage features on façades and roofs. Finally, we also demonstrate the possibility of aggregating the multi-perspective damage information at building level.

[Spatial distribution characteristics of urban potential population in Shenyang City based on QuickBird image and GIS].

PubMed

Li, Jun-Ying; Hu, Yuan-Man; Chen, Wei; Liu, Miao; Hu, Jian-Bo; Zhong, Qiao-Lin; Lu, Ning

2012-06-01

Population is the most active factor affecting city development. To understand the distribution characteristics of urban population is of significance for making city policy decisions and for optimizing the layout of various urban infrastructures. In this paper, the information of the residential buildings in Shenyang urban area was extracted from the QuickBird remote sensing images, and the spatial distribution characteristics of the population within the Third-Ring Road of the City were analyzed, according to the social and economic statistics data. In 2010, the population density in different types of residential buildings within the Third-Ring Road of the City decreased in the order of high-storey block, mixed block, mixed garden, old multi-storey building, high-storey garden, multi-storey block, multi-storey garden, villa block, shanty, and villa garden. The vacancy rate of the buildings within the Third-Ring Road was more than 30%, meaning that the real estate market was seriously overstocked. Among the five Districts of Shenyang City, Shenhe District had the highest potential population density, while Tiexi District and Dadong District had a lower one. The gravity center of the City and its five Districts was also analyzed, which could provide basic information for locating commercial facilities and planning city infrastructure.

Analysis of Land Use Change and Urbanization in the Kucukcekmece Water Basin (Istanbul, Turkey) with Temporal Satellite Data using Remote Sensing and GIS.

PubMed

Coskun, H Gonca; Alganci, Ugur; Usta, Gokce

2008-11-13

Accurate and timely information about land use and land cover (LULC) and its changes in urban areas are crucial for urban land management decision-making, ecosystem monitoring and urban planning. Also, monitoring and representation of urban sprawl and its effects on the LULC patterns and hydrological processes of an urbanized watershed is an essential part of water resource planning and management. This paper presents an image analysis study using multi temporal digital satellite imagery of LULC and changes in the Kucukcekmece Watershed (Metropolitan Istanbul, Turkey) from 1992 to 2006. The Kucukcekmece Basin includes portions of the Kucukcekmece District within the municipality of Istanbul so it faces a dramatic urbanization. An urban monitoring analysis approach was first used to implement a land cover classification. A change detection method controlled with ground truth information was then used to determine changes in land cover. During the study period, the variability and magnitude of hydrological components based on land-use patterns were cumulatively influenced by urban sprawl in the watershed. The proposed approach, which uses a combination of Remote Sensing (RS) and Geographical Information System (GIS) techniques, is an effective tool that enhances land-use monitoring, planning, and management of urbanized watersheds.

Analysis of Land Use Change and Urbanization in the Kucukcekmece Water Basin (Istanbul, Turkey) with Temporal Satellite Data using Remote Sensing and GIS

PubMed Central

Coskun, H. Gonca; Alganci, Ugur; Usta, Gokce

2008-01-01

Accurate and timely information about land use and land cover (LULC) and its changes in urban areas are crucial for urban land management decision-making, ecosystem monitoring and urban planning. Also, monitoring and representation of urban sprawl and its effects on the LULC patterns and hydrological processes of an urbanized watershed is an essential part of water resource planning and management. This paper presents an image analysis study using multi temporal digital satellite imagery of LULC and changes in the Kucukcekmece Watershed (Metropolitan Istanbul, Turkey) from 1992 to 2006. The Kucukcekmece Basin includes portions of the Kucukcekmece District within the municipality of Istanbul so it faces a dramatic urbanization. An urban monitoring analysis approach was first used to implement a land cover classification. A change detection method controlled with ground truth information was then used to determine changes in land cover. During the study period, the variability and magnitude of hydrological components based on land-use patterns were cumulatively influenced by urban sprawl in the watershed. The proposed approach, which uses a combination of Remote Sensing (RS) and Geographical Information System (GIS) techniques, is an effective tool that enhances land-use monitoring, planning, and management of urbanized watersheds. PMID:27873924

Comparison of noncontact infrared and remote sensor thermometry in normal and dry eye patients.

PubMed

Singh, G; Singh Bhinder, H

To evaluate the role of closed chamber infrared and remote sensor thermometry in normal and dry eye patients. The study was conducted on 51 dry eye cases (102 eyes), 26 men and 25 women aged 19 to 65 years (35.3614.36), and 51 normal (102 eyes) age- and sex-matched control subjects. The criteria for dry eye were Schirm e r-1 (<10 m/5 min), FTBUT (<10 sec), nd lissamine green score (>2). The remote sensor and infrared thermometry was done in losed chamber around the eye in closed and open eye positions. In normal eyes, closed chamber infrared thermometry recorded temperature 34.770.37 C in closed eye position and 35.020.39 C in open eye position as compared to 27.912.46 C in closed eye position and 28.012.46 C in open position with remote sensor thermometry. The difference in temperature from closed to open position was 0.250.90 C in infrared thermometry and 0.100.00 C with remote sensor thermometry, which was statistically significant (p<0.0000). In dry eye, the infrared therm o m e t ry recorded 35.080.61 C temperature in closed eye position and 35.530.63 C in open eye position as compared to 27.412.48 C in open and closed eye position with remote sensor thermometry. The difference in temperature from closed to open eye position was 0.450.14 C (p<0.0000) with infrared thermometry as compared to no change 0.000.00 C with remote sensor thermometry (p<0.0000). Remote sensor thermometry proved better for diagnosis of dry eye disease as it showed no change in temperature under closed chamber in closed and open position (p=0.0000). Infrared thermometry was better in recording the absolute temperature from any point on the eye.

Comparison of noncontact infrared and remote sensor thermometry in normal and dry eye patients.

PubMed

Singh, G; Bhinder, H Singh

2005-01-01

To evaluate the role of closed chamber infrared and remote sensor thermometry in normal and dry eye patients. The study was conducted on 51 dry eye cases (102 eyes), 26 men and 25 women aged 19 to 65 years (35.36+/-14.36), and 51 normal (102 eyes) age- and sex-matched control subjects. The criteria for dry eye were Schirmer-1 (<10 m/5 min), FTBUT (<10 sec), nd lissamine green score (>2). The remote sensor and infrared thermometry was done in closed chamber around the eye in closed and open eye positions. In normal eyes, closed chamber infrared thermometry recorded temperature 34.77+/-0.37 degrees C in closed eye position and 35.02+/-0.39 degrees C in open eye position as compared to 27.91+/-2.46 degrees C in closed eye position and 28.01+/-2.46 degrees C in open position with remote sensor thermometry. The difference in temperature from closed to open position was 0.25+/-0.90 degrees C in infrared thermometry and 0.10+/-0.00 degrees C with remote sensor thermometry, which was statistically significant (p<0.0000). In dry eye, the infrared thermometry recorded 35.08+/-0.61 degrees C temperature in closed eye position and 35.53+/-0.63 degrees C in open eye position as compared to 27.41+/-2.48 degrees C in open and closed eye position with remote sensor thermometry. The difference in temperature from closed to open eye position was 0.45+/-0.14 degrees C (p<0.0000) with infrared thermometry as compared to no change 0.00+/-0.00 degrees C with remote sensor thermometry (p<0.0000). Remote sensor thermometry proved better for diagnosis of dry eye disease as it showed no change in temperature under closed chamber in closed and open position (p=0.0000). Infrared thermometry was better in recording the absolute temperature from any point on the eye.

A DNA-based semantic fusion model for remote sensing data.

PubMed

Sun, Heng; Weng, Jian; Yu, Guangchuang; Massawe, Richard H

2013-01-01

Semantic technology plays a key role in various domains, from conversation understanding to algorithm analysis. As the most efficient semantic tool, ontology can represent, process and manage the widespread knowledge. Nowadays, many researchers use ontology to collect and organize data's semantic information in order to maximize research productivity. In this paper, we firstly describe our work on the development of a remote sensing data ontology, with a primary focus on semantic fusion-driven research for big data. Our ontology is made up of 1,264 concepts and 2,030 semantic relationships. However, the growth of big data is straining the capacities of current semantic fusion and reasoning practices. Considering the massive parallelism of DNA strands, we propose a novel DNA-based semantic fusion model. In this model, a parallel strategy is developed to encode the semantic information in DNA for a large volume of remote sensing data. The semantic information is read in a parallel and bit-wise manner and an individual bit is converted to a base. By doing so, a considerable amount of conversion time can be saved, i.e., the cluster-based multi-processes program can reduce the conversion time from 81,536 seconds to 4,937 seconds for 4.34 GB source data files. Moreover, the size of result file recording DNA sequences is 54.51 GB for parallel C program compared with 57.89 GB for sequential Perl. This shows that our parallel method can also reduce the DNA synthesis cost. In addition, data types are encoded in our model, which is a basis for building type system in our future DNA computer. Finally, we describe theoretically an algorithm for DNA-based semantic fusion. This algorithm enables the process of integration of the knowledge from disparate remote sensing data sources into a consistent, accurate, and complete representation. This process depends solely on ligation reaction and screening operations instead of the ontology.

A DNA-Based Semantic Fusion Model for Remote Sensing Data

PubMed Central

Sun, Heng; Weng, Jian; Yu, Guangchuang; Massawe, Richard H.

2013-01-01

Semantic technology plays a key role in various domains, from conversation understanding to algorithm analysis. As the most efficient semantic tool, ontology can represent, process and manage the widespread knowledge. Nowadays, many researchers use ontology to collect and organize data's semantic information in order to maximize research productivity. In this paper, we firstly describe our work on the development of a remote sensing data ontology, with a primary focus on semantic fusion-driven research for big data. Our ontology is made up of 1,264 concepts and 2,030 semantic relationships. However, the growth of big data is straining the capacities of current semantic fusion and reasoning practices. Considering the massive parallelism of DNA strands, we propose a novel DNA-based semantic fusion model. In this model, a parallel strategy is developed to encode the semantic information in DNA for a large volume of remote sensing data. The semantic information is read in a parallel and bit-wise manner and an individual bit is converted to a base. By doing so, a considerable amount of conversion time can be saved, i.e., the cluster-based multi-processes program can reduce the conversion time from 81,536 seconds to 4,937 seconds for 4.34 GB source data files. Moreover, the size of result file recording DNA sequences is 54.51 GB for parallel C program compared with 57.89 GB for sequential Perl. This shows that our parallel method can also reduce the DNA synthesis cost. In addition, data types are encoded in our model, which is a basis for building type system in our future DNA computer. Finally, we describe theoretically an algorithm for DNA-based semantic fusion. This algorithm enables the process of integration of the knowledge from disparate remote sensing data sources into a consistent, accurate, and complete representation. This process depends solely on ligation reaction and screening operations instead of the ontology. PMID:24116207

Geospatial Technologies as a Vehicle for Enhancing Graduate Education and Promoting the Value of Geography

ERIC Educational Resources Information Center

Oberle, Alex P.; Joseph, Sue A.; May, David W.

2010-01-01

Geospatial technologies (GSTs), such as geographic information systems, global positioning systems and remote sensing, present an avenue for expanding the already strong interdisciplinary nature of geography. This paper discusses how GSTs served as a common thread for a crosscutting faculty institute that was established to enhance graduate…

The Sky's the Limit: Integrating Geospatial Tools with Pre-College Youth Education

ERIC Educational Resources Information Center

McGee, John; Kirwan, Jeff

2010-01-01

Geospatial tools, which include global positioning systems (GPS), geographic information systems (GIS), and remote sensing, are increasingly driving a variety of applications. Local governments and private industry are embracing these tools, and the public is beginning to demand geospatial services. The U.S. Department of Labor (DOL) reported that…

Geography Education in Asia: Samples from Different Countries and Turkey

ERIC Educational Resources Information Center

Incekara, Suleyman

2010-01-01

With the maximum use of the technology such as geographic information science (GIS), remote sensing (RS), and global positioning systems (GPSs) in geography courses, along with its integrative perspective on the social and life sciences and an emphasis on student-centered education, problem solving, and sustainable and environmental education,…

Compositing multitemporal remote sensing data sets

USGS Publications Warehouse

Qi, J.; Huete, A.R.; Hood, J.; Kerr, Y.

1993-01-01

To eliminate cloud and atmosphere-affected pixels, the compositing of multi temporal remote sensing data sets is done by selecting the maximum vale of the normalized different vegetation index (NDVI) within a compositing period. The NDVI classifier, however, is strongly affected by surface type and anisotropic properties, sensor viewing geometries, and atmospheric conditions. Consequently, the composited, multi temporal, remote sensing data contain substantial noise from these external conditions. Consequently, the composited, multi temporal, remote sensing data contain substantial noise from these external effects. To improve the accuracy of compositing products, two key approaches can be taken: one is to refine the compositing classifier (NDVI) and the other is to improve existing compositing algorithms. In this project, an alternative classifier was developed and an alternative pixel selection criterion was proposed for compositing. The new classifier and the alternative compositing algorithm were applied to an advanced very high resolution radiometer data set of different biome types in the United States. The results were compared with the maximum value compositing and the best index slope extraction algorithms. The new approaches greatly reduced the high frequency noises related to the external factors and repainted more reliable data. The results suggest that the geometric-optical canopy properties of specific biomes may be needed in compositing. Limitations of the new approaches include the dependency of pixel selection on the length of the composite period and data discontinuity.

Analysis of flood inundation in ungauged basins based on multi-source remote sensing data.

PubMed

Gao, Wei; Shen, Qiu; Zhou, Yuehua; Li, Xin

2018-02-09

Floods are among the most expensive natural hazards experienced in many places of the world and can result in heavy losses of life and economic damages. The objective of this study is to analyze flood inundation in ungauged basins by performing near-real-time detection with flood extent and depth based on multi-source remote sensing data. Via spatial distribution analysis of flood extent and depth in a time series, the inundation condition and the characteristics of flood disaster can be reflected. The results show that the multi-source remote sensing data can make up the lack of hydrological data in ungauged basins, which is helpful to reconstruct hydrological sequence; the combination of MODIS (moderate-resolution imaging spectroradiometer) surface reflectance productions and the DFO (Dartmouth Flood Observatory) flood database can achieve the macro-dynamic monitoring of the flood inundation in ungauged basins, and then the differential technique of high-resolution optical and microwave images before and after floods can be used to calculate flood extent to reflect spatial changes of inundation; the monitoring algorithm for the flood depth combining RS and GIS is simple and easy and can quickly calculate the depth with a known flood extent that is obtained from remote sensing images in ungauged basins. Relevant results can provide effective help for the disaster relief work performed by government departments.

Remote counseling using HyperMirror quasi space-sharing system

NASA Astrophysics Data System (ADS)

Hashimoto, Sayuri; Morikawa, Osamu; Hashimoto, Nobuyuki; Maesako, Takanori

2008-08-01

In the modern information society, networks are getting faster, costs are getting lower, and displays are getting clearer. Today, just about anyone can easily use precise, dynamic, image distribution systems in their everyday life. Now, the question is how to give the benefits of network systems to the local community, as well as to each individual.This study was designed to use communication with realistic sensations to examine the effectiveness of remote individual counseling intervention in reducing depression, anxiety and stress in child-rearing mothers. Three child-rearing mothers residing in the city of Osaka each received one session of remote counseling intervention. The results showed an alleviation of stress related to child-rearing, i.e., the reduction in state anxiety, depression and subjective stress related to child-rearing. Moreover, an experimental demonstration employed a HyperMirror system capable of presenting visual and auditory images similar to reality, in order to provide the counselees with realistic sensations. While the voice communication environment was poor, the remote counseling allowed for the communication of sensory information, i.e., skinship that communicated information related to assurance/peace of mind, and auditory information, i.e., a whispering voices in which signals of affection were transmitted; the realistic sensation contributed to a reduction in stress levels. The positive effects of the intervention were confirmed through a pre and post intervention study. The results suggested the need to conduct future studies to confirm the mid- and long-term improvements caused by the intervention, as well as the need to improve the voice transmission environment.

Multi-dimensional super-resolution imaging enables surface hydrophobicity mapping

NASA Astrophysics Data System (ADS)

Bongiovanni, Marie N.; Godet, Julien; Horrocks, Mathew H.; Tosatto, Laura; Carr, Alexander R.; Wirthensohn, David C.; Ranasinghe, Rohan T.; Lee, Ji-Eun; Ponjavic, Aleks; Fritz, Joelle V.; Dobson, Christopher M.; Klenerman, David; Lee, Steven F.

2016-12-01

Super-resolution microscopy allows biological systems to be studied at the nanoscale, but has been restricted to providing only positional information. Here, we show that it is possible to perform multi-dimensional super-resolution imaging to determine both the position and the environmental properties of single-molecule fluorescent emitters. The method presented here exploits the solvatochromic and fluorogenic properties of nile red to extract both the emission spectrum and the position of each dye molecule simultaneously enabling mapping of the hydrophobicity of biological structures. We validated this by studying synthetic lipid vesicles of known composition. We then applied both to super-resolve the hydrophobicity of amyloid aggregates implicated in neurodegenerative diseases, and the hydrophobic changes in mammalian cell membranes. Our technique is easily implemented by inserting a transmission diffraction grating into the optical path of a localization-based super-resolution microscope, enabling all the information to be extracted simultaneously from a single image plane.

Multi-dimensional super-resolution imaging enables surface hydrophobicity mapping

PubMed Central

Bongiovanni, Marie N.; Godet, Julien; Horrocks, Mathew H.; Tosatto, Laura; Carr, Alexander R.; Wirthensohn, David C.; Ranasinghe, Rohan T.; Lee, Ji-Eun; Ponjavic, Aleks; Fritz, Joelle V.; Dobson, Christopher M.; Klenerman, David; Lee, Steven F.

2016-01-01

Super-resolution microscopy allows biological systems to be studied at the nanoscale, but has been restricted to providing only positional information. Here, we show that it is possible to perform multi-dimensional super-resolution imaging to determine both the position and the environmental properties of single-molecule fluorescent emitters. The method presented here exploits the solvatochromic and fluorogenic properties of nile red to extract both the emission spectrum and the position of each dye molecule simultaneously enabling mapping of the hydrophobicity of biological structures. We validated this by studying synthetic lipid vesicles of known composition. We then applied both to super-resolve the hydrophobicity of amyloid aggregates implicated in neurodegenerative diseases, and the hydrophobic changes in mammalian cell membranes. Our technique is easily implemented by inserting a transmission diffraction grating into the optical path of a localization-based super-resolution microscope, enabling all the information to be extracted simultaneously from a single image plane. PMID:27929085

Towards a Multi-Resolution Model of Seismic Risk in Central Asia. Challenge and perspectives

NASA Astrophysics Data System (ADS)

Pittore, M.; Wieland, M.; Bindi, D.; Parolai, S.

2011-12-01

Assessing seismic risk, defined as the probability of occurrence of economical and social losses as consequence of an earthquake, both at regional and at local scale is a challenging, multi-disciplinary task. In order to provide a reliable estimate, diverse information must be gathered by seismologists, geologists, engineers and civil authorities, and carefully integrated keeping into account the different levels of uncertainty. The research towards an integrated methodology, able to seamlessly describe seismic risk at different spatial scales is challenging, but discloses new application perspectives, particularly in those countries which suffer from a relevant seismic hazard but do not have resources for a standard assessment. Central Asian countries in particular, which exhibit one of the highest seismic hazard in the world, are experiencing a steady demographic growth, often accompanied by informal settlement and urban sprawling. A reliable evaluation of how these factors affect the seismic risk, together with a realistic assessment of the assets exposed to seismic hazard and their structural vulnerability is of particular importance, in order to undertake proper mitigation actions and to promptly and efficiently react to a catastrophic event. New strategies are needed to efficiently cope with systematic lack of information and uncertainties. An original approach is presented to assess seismic risk based on integration of information coming from remote-sensing and ground-based panoramic imaging, in situ measurements, expert knowledge and already available data. Efficient sampling strategies based on freely available medium-resolution multi-spectral satellite images are adopted to optimize data collection and validation, in a multi-scale approach. Panoramic imaging is also considered as a valuable ground-based visual data collection technique, suitable both for manual and automatic analysis. A full-probabilistic framework based on Bayes Network is proposed to integrate available information taking into account both aleatory and epistemic uncertainties. An improved risk model for the capital of Kyrgyz Republic, Biskek, has been developed following this approach and tested based on different earthquake scenarios. Preliminary results will be presented and discussed.

Evaluation of AirMSPI photopolarimetric retrievals of smoke properties with in-situ observations collected during the ImPACT-PM field campaign

NASA Astrophysics Data System (ADS)

Kalashnikova, O. V.; Garay, M. J.; Xu, F.; Seidel, F.; Diner, D. J.; Seinfeld, J.; Bates, K. H.; Kong, W.; Kenseth, C.; Cappa, C. D.

2017-12-01

We introduce and evaluate an approach for obtaining closure between in situ and polarimetric remote sensing observations of smoke properties obtained during the collocated CIRPAS Twin Otter and ER-2 aircraft measurements of the Lebec fire event on July 8, 2016. We investigate the utility of multi-angle, spectropolarimetric remote sensing imagery to evaluate the relative contribution of organics, non-organic and black carbon particles to smoke particulate composition. The remote sensing data were collected during the Imaging Polarimetric and Characterization of Tropospheric Particular Matter (ImPACT-PM) field campaign by the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI), which flew on NASA's high-altitude ER-2 aircraft. The ImPACT-PM field campaign was a joint JPL/Caltech effort to combine measurements from the Terra Multi-angle Imaging SpectroRadiometer (MISR), AirMSPI, in situ airborne measurements, and a chemical transport model to validate remote sensing retrievals of different types of airborne particulate matter with a particular emphasis on carbonaceous aerosols. The in-situ aerosol data were collected with a suite of Caltech instruments on board the CIRPAS Twin Otter aircraft and included the Aerosol Mass Spectrometer (AMS), the Differential Mobility Analyzer (DMA), and the Single Particle Soot Photometer (SP-2). The CIRPAS Twin Otter aircraft was also equipped with the Particle Soot Absorption Photometer (PSAP), nephelometer, a particle counter, and meteorological sensors. We found that the multi-angle polarimetric observations are capable of fire particulate emission monitoring by particle type as inferred from the in-situ airborne measurements. Modeling of retrieval sensitivities show that the characterization of black carbon is the most challenging. The work aims at evaluating multi-angle, spectropolarimetric capabilities for particulate matter characterization in support of the Multi-Angle Imager for Aerosols (MAIA) satellite investigation, which is currently in development under NASA's third Earth Venture Instrument Program.

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.

Knowledge, Skills, and Abilities for Entry-Level Business Analytics Positions: A Multi-Method Study

ERIC Educational Resources Information Center

Cegielski, Casey G.; Jones-Farmer, L. Allison

2016-01-01

It is impossible to deny the significant impact from the emergence of big data and business analytics on the fields of Information Technology, Quantitative Methods, and the Decision Sciences. Both industry and academia seek to hire talent in these areas with the hope of developing organizational competencies. This article describes a multi-method…

An Improvement of Robust Biometrics-Based Authentication and Key Agreement Scheme for Multi-Server Environments Using Smart Cards

PubMed Central

Moon, Jongho; Choi, Younsung; Jung, Jaewook; Won, Dongho

2015-01-01

In multi-server environments, user authentication is a very important issue because it provides the authorization that enables users to access their data and services; furthermore, remote user authentication schemes for multi-server environments have solved the problem that has arisen from user’s management of different identities and passwords. For this reason, numerous user authentication schemes that are designed for multi-server environments have been proposed over recent years. In 2015, Lu et al. improved upon Mishra et al.’s scheme, claiming that their remote user authentication scheme is more secure and practical; however, we found that Lu et al.’s scheme is still insecure and incorrect. In this paper, we demonstrate that Lu et al.’s scheme is vulnerable to outsider attack and user impersonation attack, and we propose a new biometrics-based scheme for authentication and key agreement that can be used in multi-server environments; then, we show that our proposed scheme is more secure and supports the required security properties. PMID:26709702

Scene text detection by leveraging multi-channel information and local context

NASA Astrophysics Data System (ADS)

Wang, Runmin; Qian, Shengyou; Yang, Jianfeng; Gao, Changxin

2018-03-01

As an important information carrier, texts play significant roles in many applications. However, text detection in unconstrained scenes is a challenging problem due to cluttered backgrounds, various appearances, uneven illumination, etc.. In this paper, an approach based on multi-channel information and local context is proposed to detect texts in natural scenes. According to character candidate detection plays a vital role in text detection system, Maximally Stable Extremal Regions(MSERs) and Graph-cut based method are integrated to obtain the character candidates by leveraging the multi-channel image information. A cascaded false positive elimination mechanism are constructed from the perspective of the character and the text line respectively. Since the local context information is very valuable for us, these information is utilized to retrieve the missing characters for boosting the text detection performance. Experimental results on two benchmark datasets, i.e., the ICDAR 2011 dataset and the ICDAR 2013 dataset, demonstrate that the proposed method have achieved the state-of-the-art performance.

Allometric constraints to inversion of canopy structure from remote sensing

NASA Astrophysics Data System (ADS)

Wolf, A.; Berry, J. A.; Asner, G. P.

2008-12-01

Canopy radiative transfer models employ a large number of vegetation architectural and leaf biochemical attributes. Studies of leaf biochemistry show a wide array of chemical and spectral diversity that suggests that several leaf biochemical constituents can be independently retrieved from multi-spectral remotely sensed imagery. In contrast, attempts to exploit multi-angle imagery to retrieve canopy structure only succeed in finding two or three of the many unknown canopy arhitectural attributes. We examine a database of over 5000 destructive tree harvests from Eurasia to show that allometry - the covariation of plant form across a broad range of plant size and canopy density - restricts the architectural diversity of plant canopies into a single composite variable ranging from young canopies with many short trees with small crowns to older canopies with fewer trees and larger crowns. Moreover, these architectural attributes are closely linked to biomass via allometric constraints such as the "self-thinning law". We use the measured variance and covariance of plant canopy architecture in these stands to drive the radiative transfer model DISORD, which employs the Li-Strahler geometric optics model. This correlations introduced in the Monte Carlo study are used to determine which attributes of canopy architecture lead to important variation that can be observed by multi-angle or multi-spectral satellite observations, using the sun-view geometry characteristic of MODIS observations in different biomes located at different latitude bands. We conclude that although multi-angle/multi-spectral remote sensing is only sensitive to some of the many unknown canopy attributes that ecologists would wish to know, the strong allometric covariation between these attributes and others permits a large number of inferrences, such as forest biomass, that will be meaningful next-generation vegetation products useful for data assimilation.

Adjoint Methods for Adjusting Three-Dimensional Atmosphere and Surface Properties to Fit Multi-Angle Multi-Pixel Polarimetric Measurements

NASA Technical Reports Server (NTRS)

Martin, William G.; Cairns, Brian; Bal, Guillaume

2014-01-01

This paper derives an efficient procedure for using the three-dimensional (3D) vector radiative transfer equation (VRTE) to adjust atmosphere and surface properties and improve their fit with multi-angle/multi-pixel radiometric and polarimetric measurements of scattered sunlight. The proposed adjoint method uses the 3D VRTE to compute the measurement misfit function and the adjoint 3D VRTE to compute its gradient with respect to all unknown parameters. In the remote sensing problems of interest, the scalar-valued misfit function quantifies agreement with data as a function of atmosphere and surface properties, and its gradient guides the search through this parameter space. Remote sensing of the atmosphere and surface in a three-dimensional region may require thousands of unknown parameters and millions of data points. Many approaches would require calls to the 3D VRTE solver in proportion to the number of unknown parameters or measurements. To avoid this issue of scale, we focus on computing the gradient of the misfit function as an alternative to the Jacobian of the measurement operator. The resulting adjoint method provides a way to adjust 3D atmosphere and surface properties with only two calls to the 3D VRTE solver for each spectral channel, regardless of the number of retrieval parameters, measurement view angles or pixels. This gives a procedure for adjusting atmosphere and surface parameters that will scale to the large problems of 3D remote sensing. For certain types of multi-angle/multi-pixel polarimetric measurements, this encourages the development of a new class of three-dimensional retrieval algorithms with more flexible parametrizations of spatial heterogeneity, less reliance on data screening procedures, and improved coverage in terms of the resolved physical processes in the Earth?s atmosphere.

Earthquake Building Damage Mapping Based on Feature Analyzing Method from Synthetic Aperture Radar Data

NASA Astrophysics Data System (ADS)

An, L.; Zhang, J.; Gong, L.

2018-04-01

Playing an important role in gathering information of social infrastructure damage, Synthetic Aperture Radar (SAR) remote sensing is a useful tool for monitoring earthquake disasters. With the wide application of this technique, a standard method, comparing post-seismic to pre-seismic data, become common. However, multi-temporal SAR processes, are not always achievable. To develop a post-seismic data only method for building damage detection, is of great importance. In this paper, the authors are now initiating experimental investigation to establish an object-based feature analysing classification method for building damage recognition.

Land-Ocean-Atmospheric Coupling Associated with Earthquakes

NASA Astrophysics Data System (ADS)

Prasad, A. K.; Singh, R. P.; Kumar, S.; Cervone, G.; Kafatos, M.; Zlotnicki, J.

2007-12-01

Earthquakes are well known to occur along the plate boundaries and also on the stable shield. The recent studies have shown existence of strong coupling between land-ocean-atmospheric parameters associated with the earthquakes. We have carried out detailed analysis of multi sensor data (optical and microwave remote) to show existence of strong coupling between land-ocean-atmospheric parameters associated with the earthquakes with focal depth up to 30 km and magnitude greater than 5.5. Complimentary nature of various land, ocean and atmospheric parameters will be demonstrated in getting an early warning information about an impending earthquake.

Evaluation of MuSyQ land surface albedo based on LAnd surface Parameters VAlidation System (LAPVAS)

NASA Astrophysics Data System (ADS)

Dou, B.; Wen, J.; Xinwen, L.; Zhiming, F.; Wu, S.; Zhang, Y.

2016-12-01

satellite derived Land surface albedo is an essential climate variable which controls the earth energy budget and it can be used in applications such as climate change, hydrology, and numerical weather prediction. However, the accuracy and uncertainty of surface albedo products should be evaluated with a reliable reference truth data prior to applications. A new comprehensive and systemic project of china, called the Remote Sensing Application Network (CRSAN), has been launched recent years. Two subjects of this project is developing a Multi-source data Synergized Quantitative Remote Sensin g Production System ( MuSyQ ) and a Web-based validation system named LAnd surface remote sensing Product VAlidation System (LAPVAS) , which aims to generate a quantitative remote sensing product for ecosystem and environmental monitoring and validate them with a reference validation data and a standard validation system, respectively. Land surface BRDF/albedo is one of product datasets of MuSyQ which has a pentad period with 1km spatial resolution and is derived by Multi-sensor Combined BRDF Inversion ( MCBI ) Model. In this MuSyQ albedo evaluation, a multi-validation strategy is implemented by LAPVAS, including directly and multi-scale validation with field measured albedo and cross validation with MODIS albedo product with different land cover. The results reveal that MuSyQ albedo data with a 5-day temporal resolution is in higher sensibility and accuracy during land cover change period, e.g. snowing. But results without regard to snow or changed land cover, MuSyQ albedo generally is in similar accuracy with MODIS albedo and meet the climate modeling requirement of an absolute accuracy of 0.05.

Multispectral, Fluorescent and Photoplethysmographic Imaging for Remote Skin Assessment

PubMed Central

Spigulis, Janis

2017-01-01

Optical tissue imaging has several advantages over the routine clinical imaging methods, including non-invasiveness (it does not change the structure of tissues), remote operation (it avoids infections) and the ability to quantify the tissue condition by means of specific image parameters. Dermatologists and other skin experts need compact (preferably pocket-size), self-sustaining and easy-to-use imaging devices. The operational principles and designs of ten portable in-vivo skin imaging prototypes developed at the Biophotonics Laboratory of Institute of Atomic Physics and Spectroscopy, University of Latvia during the recent five years are presented in this paper. Four groups of imaging devices are considered. Multi-spectral imagers offer possibilities for distant mapping of specific skin parameters, thus facilitating better diagnostics of skin malformations. Autofluorescence intensity and photobleaching rate imagers show a promising potential for skin tumor identification and margin delineation. Photoplethysmography video-imagers ensure remote detection of cutaneous blood pulsations and can provide real-time information on cardiovascular parameters and anesthesia efficiency. Multimodal skin imagers perform several of the abovementioned functions by taking a number of spectral and video images with the same image sensor. Design details of the developed prototypes and results of clinical tests illustrating their functionality are presented and discussed. PMID:28534815

Thermal anomaly before earthquake and damage assessment using remote sensing data for 2014 Yutian earthquake

NASA Astrophysics Data System (ADS)

Zhang, Yanmei; Huang, Haiying; Jiang, Zaisen; Fang, Ying; Cheng, Xiao

2014-12-01

Thermal anomaly appears to be a significant precursor of some strong earthquakes. In this study, time series of MODIS Land Surface Temperature (LST) products from 2001 to 2014 are processed and analyzed to locate possible anomalies prior to the Yutian earthquake (12 February 2014, Xinjiang, CHINA). In order to reduce the seasonal or annual effects from the LST variations, also to avoid the rainy and cloudy weather in this area, a background mean of ten-day nighttime LST are derived using averaged MOD11A2 products from 2001 to 2012. Then the ten-day LST data from Jan 2014 to FebJanuary 2014 were differenced using the above background. Abnormal LST increase before the earthquake is quite obvious from the differential images, indicating that this method is useful in such area with high mountains and wide-area deserts. Also, in order to assess the damage to infrastructure, China's latest civilian high-resolution remote sensing satellite - GF-1 remote sensed data are applied to the affected counties in this area. The damaged infrastructures and ground surface could be easily interpreted in the fused pan-chromatic and multi-spectral images integrating both texture and spectral information.

System and method for evaluating wind flow fields using remote sensing devices

DOEpatents

Schroeder, John; Hirth, Brian; Guynes, Jerry

2016-12-13

The present invention provides a system and method for obtaining data to determine one or more characteristics of a wind field using a first remote sensing device and a second remote sensing device. Coordinated data is collected from the first and second remote sensing devices and analyzed to determine the one or more characteristics of the wind field. The first remote sensing device is positioned to have a portion of the wind field within a first scanning sector of the first remote sensing device. The second remote sensing device is positioned to have the portion of the wind field disposed within a second scanning sector of the second remote sensing device.

Multi- and hyperspectral geologic remote sensing: A review

NASA Astrophysics Data System (ADS)

van der Meer, Freek D.; van der Werff, Harald M. A.; van Ruitenbeek, Frank J. A.; Hecker, Chris A.; Bakker, Wim H.; Noomen, Marleen F.; van der Meijde, Mark; Carranza, E. John M.; Smeth, J. Boudewijn de; Woldai, Tsehaie

2012-02-01

Geologists have used remote sensing data since the advent of the technology for regional mapping, structural interpretation and to aid in prospecting for ores and hydrocarbons. This paper provides a review of multispectral and hyperspectral remote sensing data, products and applications in geology. During the early days of Landsat Multispectral scanner and Thematic Mapper, geologists developed band ratio techniques and selective principal component analysis to produce iron oxide and hydroxyl images that could be related to hydrothermal alteration. The advent of the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) with six channels in the shortwave infrared and five channels in the thermal region allowed to produce qualitative surface mineral maps of clay minerals (kaolinite, illite), sulfate minerals (alunite), carbonate minerals (calcite, dolomite), iron oxides (hematite, goethite), and silica (quartz) which allowed to map alteration facies (propylitic, argillic etc.). The step toward quantitative and validated (subpixel) surface mineralogic mapping was made with the advent of high spectral resolution hyperspectral remote sensing. This led to a wealth of techniques to match image pixel spectra to library and field spectra and to unravel mixed pixel spectra to pure endmember spectra to derive subpixel surface compositional information. These products have found their way to the mining industry and are to a lesser extent taken up by the oil and gas sector. The main threat for geologic remote sensing lies in the lack of (satellite) data continuity. There is however a unique opportunity to develop standardized protocols leading to validated and reproducible products from satellite remote sensing for the geology community. By focusing on geologic mapping products such as mineral and lithologic maps, geochemistry, P-T paths, fluid pathways etc. the geologic remote sensing community can bridge the gap with the geosciences community. Increasingly workflows should be multidisciplinary and remote sensing data should be integrated with field observations and subsurface geophysical data to monitor and understand geologic processes.

A combined field/remote sensing approach for characterizing landslide risk in coastal areas

NASA Astrophysics Data System (ADS)

Francioni, Mirko; Coggan, John; Eyre, Matthew; Stead, Doug

2018-05-01

Understanding the key factors controlling slope failure mechanisms in coastal areas is the first and most important step for analyzing, reconstructing and predicting the scale, location and extent of future instability in rocky coastlines. Different failure mechanisms may be possible depending on the influence of the engineering properties of the rock mass (including the fracture network), the persistence and type of discontinuity and the relative aspect or orientation of the coastline. Using a section of the North Coast of Cornwall, UK, as an example we present a multi-disciplinary approach for characterizing landslide risk associated with coastal instabilities in a blocky rock mass. Remotely captured terrestrial and aerial LiDAR and photogrammetric data were interrogated using Geographic Information System (GIS) techniques to provide a framework for subsequent analysis, interpretation and validation. The remote sensing mapping data was used to define the rock mass discontinuity network of the area and to differentiate between major and minor geological structures controlling the evolution of the North Coast of Cornwall. Kinematic instability maps generated from aerial LiDAR data using GIS techniques and results from structural and engineering geological surveys are presented. With this method, it was possible to highlight the types of kinematic failure mechanism that may generate coastal landslides and highlight areas that are more susceptible to instability or increased risk of future instability. Multi-temporal aerial LiDAR data and orthophotos were also studied using GIS techniques to locate recent landslide failures, validate the results obtained from the kinematic instability maps through site observations and provide improved understanding of the factors controlling the coastal geomorphology. The approach adopted is not only useful for academic research, but also for local authorities and consultancy's when assessing the likely risks of coastal instability.

Real-time visual communication to aid disaster recovery in a multi-segment hybrid wireless networking system

NASA Astrophysics Data System (ADS)

Al Hadhrami, Tawfik; Wang, Qi; Grecos, Christos

2012-06-01

When natural disasters or other large-scale incidents occur, obtaining accurate and timely information on the developing situation is vital to effective disaster recovery operations. High-quality video streams and high-resolution images, if available in real time, would provide an invaluable source of current situation reports to the incident management team. Meanwhile, a disaster often causes significant damage to the communications infrastructure. Therefore, another essential requirement for disaster management is the ability to rapidly deploy a flexible incident area communication network. Such a network would facilitate the transmission of real-time video streams and still images from the disrupted area to remote command and control locations. In this paper, a comprehensive end-to-end video/image transmission system between an incident area and a remote control centre is proposed and implemented, and its performance is experimentally investigated. In this study a hybrid multi-segment communication network is designed that seamlessly integrates terrestrial wireless mesh networks (WMNs), distributed wireless visual sensor networks, an airborne platform with video camera balloons, and a Digital Video Broadcasting- Satellite (DVB-S) system. By carefully integrating all of these rapidly deployable, interworking and collaborative networking technologies, we can fully exploit the joint benefits provided by WMNs, WSNs, balloon camera networks and DVB-S for real-time video streaming and image delivery in emergency situations among the disaster hit area, the remote control centre and the rescue teams in the field. The whole proposed system is implemented in a proven simulator. Through extensive simulations, the real-time visual communication performance of this integrated system has been numerically evaluated, towards a more in-depth understanding in supporting high-quality visual communications in such a demanding context.

Static and Motion-Based Visual Features Used by Airport Tower Controllers: Some Implications for the Design of Remote or Virtual Towers

NASA Technical Reports Server (NTRS)

Ellis, Stephen R.; Liston, Dorion B.

2011-01-01

Visual motion and other visual cues are used by tower controllers to provide important support for their control tasks at and near airports. These cues are particularly important for anticipated separation. Some of them, which we call visual features, have been identified from structured interviews and discussions with 24 active air traffic controllers or supervisors. The visual information that these features provide has been analyzed with respect to possible ways it could be presented at a remote tower that does not allow a direct view of the airport. Two types of remote towers are possible. One could be based on a plan-view, map-like computer-generated display of the airport and its immediate surroundings. An alternative would present a composite perspective view of the airport and its surroundings, possibly provided by an array of radially mounted cameras positioned at the airport in lieu of a tower. An initial more detailed analyses of one of the specific landing cues identified by the controllers, landing deceleration, is provided as a basis for evaluating how controllers might detect and use it. Understanding other such cues will help identify the information that may be degraded or lost in a remote or virtual tower not located at the airport. Some initial suggestions how some of the lost visual information may be presented in displays are mentioned. Many of the cues considered involve visual motion, though some important static cues are also discussed.

Rapid encoding of relationships between spatially remote motion signals.

PubMed

Maruya, Kazushi; Holcombe, Alex O; Nishida, Shin'ya

2013-02-06

For visual processing, the temporal correlation of remote local motion signals is a strong cue to detect meaningful large-scale structures in the retinal image, because related points are likely to move together regardless of their spatial separation. While the processing of multi-element motion patterns involved in biological motion and optic flow has been studied intensively, the encoding of simpler pairwise relationships between remote motion signals remains poorly understood. We investigated this process by measuring the temporal rate limit for perceiving the relationship of two motion directions presented at the same time at different spatial locations. Compared to luminance or orientation, motion comparison was more rapid. Performance remained very high even when interstimulus separation was increased up to 100°. Motion comparison also remained rapid regardless of whether the two motion directions were similar to or different from each other. The exception was a dramatic slowing when the elements formed an orthogonal "T," in which two motions do not perceptually group together. Motion presented at task-irrelevant positions did not reduce performance, suggesting that the rapid motion comparison could not be ascribed to global optic flow processing. Our findings reveal the existence and unique nature of specialized processing that encodes long-range relationships between motion signals for quick appreciation of global dynamic scene structure.

Using spectral methods to obtain particle size information from optical data: applications to measurements from CARES 2010

NASA Astrophysics Data System (ADS)

Atkinson, Dean B.; Pekour, Mikhail; Chand, Duli; Radney, James G.; Kolesar, Katheryn R.; Zhang, Qi; Setyan, Ari; O'Neill, Norman T.; Cappa, Christopher D.

2018-04-01

Multi-wavelength in situ aerosol extinction, absorption and scattering measurements made at two ground sites during the 2010 Carbonaceous Aerosols and Radiative Effects Study (CARES) are analyzed using a spectral deconvolution method that allows extraction of particle-size-related information, including the fraction of extinction produced by the fine-mode particles and the effective radius of the fine mode. The spectral deconvolution method is typically applied to analysis of remote sensing measurements. Here, its application to in situ measurements allows for comparison with more direct measurement methods and validation of the retrieval approach. Overall, the retrieved fine-mode fraction and effective radius compare well with other in situ measurements, including size distribution measurements and scattering and absorption measurements made separately for PM1 and PM10, although there were some periods during which the different methods yielded different results. One key contributor to differences between the results obtained is the alternative, spectrally based definitions of fine and coarse modes from the optical methods, relative to instruments that use a physically defined cut point. These results indicate that for campaigns where size, composition and multi-wavelength optical property measurements are made, comparison of the results can result in closure or can identify unusual circumstances. The comparison here also demonstrates that in situ multi-wavelength optical property measurements can be used to determine information about particle size distributions in situations where direct size distribution measurements are not available.

Using spectral methods to obtain particle size information from optical data: applications to measurements from CARES 2010

DOE PAGES

Atkinson, Dean B.; Pekour, Mikhail; Chand, Duli; ...

2018-04-23

Here, multi-wavelength in situ aerosol extinction, absorption and scattering measurements made at two ground sites during the 2010 Carbonaceous Aerosols and Radiative Effects Study (CARES) are analyzed using a spectral deconvolution method that allows extraction of particle-size-related information, including the fraction of extinction produced by the fine-mode particles and the effective radius of the fine mode. The spectral deconvolution method is typically applied to analysis of remote sensing measurements. Here, its application to in situ measurements allows for comparison with more direct measurement methods and validation of the retrieval approach. Overall, the retrieved fine-mode fraction and effective radius compare wellmore » with other in situ measurements, including size distribution measurements and scattering and absorption measurements made separately for PM 1 and PM 10, although there were some periods during which the different methods yielded different results. One key contributor to differences between the results obtained is the alternative, spectrally based definitions of fine and coarse modes from the optical methods, relative to instruments that use a physically defined cut point. These results indicate that for campaigns where size, composition and multi-wavelength optical property measurements are made, comparison of the results can result in closure or can identify unusual circumstances. The comparison here also demonstrates that in situ multi-wavelength optical property measurements can be used to determine information about particle size distributions in situations where direct size distribution measurements are not available.« less

Using spectral methods to obtain particle size information from optical data: applications to measurements from CARES 2010

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

Atkinson, Dean B.; Pekour, Mikhail; Chand, Duli

Here, multi-wavelength in situ aerosol extinction, absorption and scattering measurements made at two ground sites during the 2010 Carbonaceous Aerosols and Radiative Effects Study (CARES) are analyzed using a spectral deconvolution method that allows extraction of particle-size-related information, including the fraction of extinction produced by the fine-mode particles and the effective radius of the fine mode. The spectral deconvolution method is typically applied to analysis of remote sensing measurements. Here, its application to in situ measurements allows for comparison with more direct measurement methods and validation of the retrieval approach. Overall, the retrieved fine-mode fraction and effective radius compare wellmore » with other in situ measurements, including size distribution measurements and scattering and absorption measurements made separately for PM 1 and PM 10, although there were some periods during which the different methods yielded different results. One key contributor to differences between the results obtained is the alternative, spectrally based definitions of fine and coarse modes from the optical methods, relative to instruments that use a physically defined cut point. These results indicate that for campaigns where size, composition and multi-wavelength optical property measurements are made, comparison of the results can result in closure or can identify unusual circumstances. The comparison here also demonstrates that in situ multi-wavelength optical property measurements can be used to determine information about particle size distributions in situations where direct size distribution measurements are not available.« less

Using spectral methods to obtain particle size information from optical data: applications to measurements from CARES 2010

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

Atkinson, Dean B.; Pekour, Mikhail; Chand, Duli

Multi-wavelength in situ aerosol extinction, absorption and scattering measurements made at two ground sites during the 2010 Carbonaceous Aerosols and Radiative Effects Study (CARES) are analyzed using a spectral deconvolution method that allows extraction of particle-size-related information, including the fraction of extinction produced by the fine-mode particles and the effective radius of the fine mode. The spectral deconvolution method is typically applied to analysis of remote sensing measurements. Here, its application to in situ measurements allows for comparison with more direct measurement methods and validation of the retrieval approach. Overall, the retrieved fine-mode fraction and effective radius compare well withmore » other in situ measurements, including size distribution measurements and scattering and absorption measurements made separately for PM 1 and PM 10, although there were some periods during which the different methods yielded different results. One key contributor to differences between the results obtained is the alternative, spectrally based definitions of fine and coarse modes from the optical methods, relative to instruments that use a physically defined cut point. These results indicate that for campaigns where size, composition and multi-wavelength optical property measurements are made, comparison of the results can result in closure or can identify unusual circumstances. The comparison here also demonstrates that in situ multi-wavelength optical property measurements can be used to determine information about particle size distributions in situations where direct size distribution measurements are not available.« less

Land use/cover classification in the Brazilian Amazon using satellite images.

PubMed

Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant'anna, Sidnei João Siqueira

2012-09-01

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

Geographic techniques and recent applications of remote sensing to landscape-water quality studies

USGS Publications Warehouse

Griffith, J.A.

2002-01-01

This article overviews recent advances in studies of landscape-water quality relationships using remote sensing techniques. With the increasing feasibility of using remotely-sensed data, landscape-water quality studies can now be more easily performed on regional, multi-state scales. The traditional method of relating land use and land cover to water quality has been extended to include landscape pattern and other landscape information derived from satellite data. Three items are focused on in this article: 1) the increasing recognition of the importance of larger-scale studies of regional water quality that require a landscape perspective; 2) the increasing importance of remotely sensed data, such as the imagery-derived normalized difference vegetation index (NDVI) and vegetation phenological metrics derived from time-series NDVI data; and 3) landscape pattern. In some studies, using landscape pattern metrics explained some of the variation in water quality not explained by land use/cover. However, in some other studies, the NDVI metrics were even more highly correlated to certain water quality parameters than either landscape pattern metrics or land use/cover proportions. Although studies relating landscape pattern metrics to water quality have had mixed results, this recent body of work applying these landscape measures and satellite-derived metrics to water quality analysis has demonstrated their potential usefulness in monitoring watershed conditions across large regions.

Human and remote sensing data to investigate the frontiers of urbanization in the south of Mexico City.

PubMed

Rodriguez Lopez, Juan Miguel; Heider, Katharina; Scheffran, Jürgen

2017-04-01

The data presented here were originally collected for the article "Frontiers of Urbanization: Identifying and Explaining Urbanization Hot Spots in the South of Mexico City Using Human and Remote Sensing" (Rodriguez et al. 2017) [4]. They were divided into three databases (remote sensing, human sensing, and census information), using a multi-method approach with the goal of analyzing the impact of urbanization on protected areas in southern Mexico City. The remote sensing database was prepared as a result of a semi-automatic classification, dividing the land cover data into urban and non-urban classes. The second data set details an alternative view of the phenomena of urbanization by concentrating on illegal settlements in the conservation zone. It was based on voluntary complaints about environmental and land use offences filed at the Procuraduria Ambiental y del Ordenamiento Territorial del Distrito Federal (PAOT), which is a governmental entity responsible for reviewing and processing grievances on five basic topics: illegal land use, deterioration of green areas, waste, noise/vibrations, and animals. Anyone can file a PAOT complaint by phone, electronically, or in person. The complaint ends with a resolution, act of conciliation, or recommendation for action by other actors, such as the police or health office. The third data about unemployment was extracted from Mexico׳s National Census 2010 database available via public access.

Land use/cover classification in the Brazilian Amazon using satellite images

PubMed Central

Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant’Anna, Sidnei João Siqueira

2013-01-01

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data. PMID:24353353

[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.

Flood risks in urbanized areas - multi-sensoral approaches using remotely sensed data for risk assessment

NASA Astrophysics Data System (ADS)

Taubenböck, H.; Wurm, M.; Netzband, M.; Zwenzner, H.; Roth, A.; Rahman, A.; Dech, S.

2011-02-01

Estimating flood risks and managing disasters combines knowledge in climatology, meteorology, hydrology, hydraulic engineering, statistics, planning and geography - thus a complex multi-faceted problem. This study focuses on the capabilities of multi-source remote sensing data to support decision-making before, during and after a flood event. With our focus on urbanized areas, sample methods and applications show multi-scale products from the hazard and vulnerability perspective of the risk framework. From the hazard side, we present capabilities with which to assess flood-prone areas before an expected disaster. Then we map the spatial impact during or after a flood and finally, we analyze damage grades after a flood disaster. From the vulnerability side, we monitor urbanization over time on an urban footprint level, classify urban structures on an individual building level, assess building stability and quantify probably affected people. The results show a large database for sustainable development and for developing mitigation strategies, ad-hoc coordination of relief measures and organizing rehabilitation.

Raft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features

NASA Astrophysics Data System (ADS)

Wang, Min; Cui, Qi; Wang, Jie; Ming, Dongping; Lv, Guonian

2017-01-01

In this paper, we first propose several novel concepts for object-based image analysis, which include line-based shape regularity, line density, and scale-based best feature value (SBV), based on the region-line primitive association framework (RLPAF). We then propose a raft cultivation area (RCA) extraction method for high spatial resolution (HSR) remote sensing imagery based on multi-scale feature fusion and spatial rule induction. The proposed method includes the following steps: (1) Multi-scale region primitives (segments) are obtained by image segmentation method HBC-SEG, and line primitives (straight lines) are obtained by phase-based line detection method. (2) Association relationships between regions and lines are built based on RLPAF, and then multi-scale RLPAF features are extracted and SBVs are selected. (3) Several spatial rules are designed to extract RCAs within sea waters after land and water separation. Experiments show that the proposed method can successfully extract different-shaped RCAs from HR images with good performance.

Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing

DTIC Science & Technology

2014-06-12

interferometry and polarimetry . In the paper, the model was used to simulate SAR data for Mangrove (tropical) and Nezer (temperate) forests for P-band and...Scattering Model Applied to Radiometry, Interferometry, and Polarimetry at P- and L-Band. IEEE Transactions on Geoscience and Remote Sensing 44(4): 849

Process and Learning Outcomes from Remotely-Operated, Simulated, and Hands-on Student Laboratories

ERIC Educational Resources Information Center

Corter, James E.; Esche, Sven K.; Chassapis, Constantin; Ma, Jing; Nickerson, Jeffrey V.

2011-01-01

A large-scale, multi-year, randomized study compared learning activities and outcomes for hands-on, remotely-operated, and simulation-based educational laboratories in an undergraduate engineering course. Students (N = 458) worked in small-group lab teams to perform two experiments involving stress on a cantilever beam. Each team conducted the…

Remote Science Operation Center research

NASA Technical Reports Server (NTRS)

Banks, P. M.

1986-01-01

Progress in the following areas is discussed: the design, planning and operation of a remote science payload operations control center; design and planning of a data link via satellite; and the design and prototyping of an advanced workstation environment for multi-media (3-D computer aided design/computer aided engineering, voice, video, text) communications and operations.

Toward irrigation retrieval by combining multi-sensor remote sensing data into a land surface model over a semi-arid region

NASA Astrophysics Data System (ADS)

Malbéteau, Y.; Lopez, O.; Houborg, R.; McCabe, M.

2017-12-01

Agriculture places considerable pressure on water resources, with the relationship between water availability and food production being critical for sustaining population growth. Monitoring water resources is particularly important in arid and semi-arid regions, where irrigation can represent up to 80% of the consumptive uses of water. In this context, it is necessary to optimize on-farm irrigation management by adjusting irrigation to crop water requirements throughout the growing season. However, in situ point measurements are not routinely available over extended areas and may not be representative at the field scale. Remote sensing approaches present as a cost-effective technique for mapping and monitoring broad areas. By taking advantage of multi-sensor remote sensing methodologies, such as those provided by MODIS, Landsat, Sentinel and Cubesats, we propose a new method to estimate irrigation input at pivot-scale. Here we explore the development of crop-water use estimates via these remote sensing data and integrate them into a land surface modeling framework, using a farm in Saudi Arabia as a demonstration of what can be achieved at larger scales.

Identification and Simulation of Subsurface Soil patterns using hidden Markov random fields and remote sensing and geophysical EMI data sets

NASA Astrophysics Data System (ADS)

Wang, Hui; Wellmann, Florian; Verweij, Elizabeth; von Hebel, Christian; van der Kruk, Jan

2017-04-01

Lateral and vertical spatial heterogeneity of subsurface properties such as soil texture and structure influences the available water and resource supply for crop growth. High-resolution mapping of subsurface structures using non-invasive geo-referenced geophysical measurements, like electromagnetic induction (EMI), enables a characterization of 3D soil structures, which have shown correlations to remote sensing information of the crop states. The benefit of EMI is that it can return 3D subsurface information, however the spatial dimensions are limited due to the labor intensive measurement procedure. Although active and passive sensors mounted on air- or space-borne platforms return 2D images, they have much larger spatial dimensions. Combining both approaches provides us with a potential pathway to extend the detailed 3D geophysical information to a larger area by using remote sensing information. In this study, we aim at extracting and providing insights into the spatial and statistical correlation of the geophysical and remote sensing observations of the soil/vegetation continuum system. To this end, two key points need to be addressed: 1) how to detect and recognize the geometric patterns (i.e., spatial heterogeneity) from multiple data sets, and 2) how to quantitatively describe the statistical correlation between remote sensing information and geophysical measurements. In the current study, the spatial domain is restricted to shallow depths up to 3 meters, and the geostatistical database contains normalized difference vegetation index (NDVI) derived from RapidEye satellite images and apparent electrical conductivities (ECa) measured from multi-receiver EMI sensors for nine depths of exploration ranging from 0-2.7 m. The integrated data sets are mapped into both the physical space (i.e. the spatial domain) and feature space (i.e. a two-dimensional space framed by the NDVI and the ECa data). Hidden Markov Random Fields (HMRF) are employed to model the underlying heterogeneities in spatial domain and finite Gaussian mixture models are adopted to quantitatively describe the statistical patterns in terms of center vectors and covariance matrices in feature space. A recently developed parallel stochastic clustering algorithm is adopted to implement the HMRF models and the Markov chain Monte Carlo based Bayesian inference. Certain spatial patterns such as buried paleo-river channels covered by shallow sediments are investigated as typical examples. The results indicate that the geometric patterns of the subsurface heterogeneity can be represented and quantitatively characterized by HMRF. Furthermore, the statistical patterns of the NDVI and the EMI data from the soil/vegetation-continuum system can be inferred and analyzed in a quantitative manner.

Impact of the Northern Studies Stream and Rehabilitation Studies programs on recruitment and retention to rural and remote practice: 2002-2010.

PubMed

Winn, Christopher S; Chisholm, Brock A; Hummelbrunner, Jackie A; Tryssenaar, Joyce; Kandler, Liane S

2015-01-01

A shortage of rehabilitation practitioners in rural and/or remote (rural/remote) practice areas has a negative impact on healthcare delivery. In Northern Ontario, Canada, a shortage of rehabilitation professionals (audiology, occupational therapy, physiotherapy, speech-language pathology) has been well documented. In response to this shortage, the Northern Studies Stream (NSS) and Rehabilitation Studies (RS) programs were developed with the mandate to increase the recruitment and retention of rehabilitation professionals to Northern Ontario. However, the number of NSS or RS program graduates who choose to live and work in Northern Ontario or other rural/remote areas, and the extent to which participation in these programs or other factors contributed to their decision, is largely unknown. Between 2002 and 2010, a total of 641 individuals participated in the NSS and RS programs and were therefore eligible to participate in the study. Current contact information was obtained for 536 of these individuals (83.6%) who were eligible to participate in the study. An internet-hosted survey was administered in June of 2011. The survey consisted of 48 questions focusing on personal and professional demographics, postgraduate practice and experience, educational preparation, and factors affecting recruitment and retention decisions. A total of 280 respondents completed the survey (response rate 52%). Of these, 95 (33.9%) reported having chosen rural or remote practice following graduation. Multiple factors predictive of recruitment and retention to rural/remote practice were identified. Of particular note was that individuals raised in a rural or remote community were 3.3 times more likely to work in a rural or remote community after graduation. Recruitment was strongly associated with length of time immersed in rural/remote education settings and to participation in the NSS academic semester. Job satisfaction, professional networking opportunities, and rural lifestyle options were identified as important factors for retention in rural/remote practice areas. The NSS and RS programs have experienced encouraging recruitment outcomes in the past 10 years. Recruitment and retention of rehabilitation therapists to rural/remote locations appears to be positively and significantly affected by the origins of the health professional. The completion of both academic and clinical education in a rural/remote setting and longer duration of rural/remote education were positively associated with an increased likelihood of choosing to practice in a rural/remote area following entry to practice. These findings have potential implications for admission criteria to rehabilitation education programs with a rural curriculum focus as well as implications for postgraduate mentorship programs and employers in rural/remote areas.

Alexnet Feature Extraction and Multi-Kernel Learning for Objectoriented Classification

NASA Astrophysics Data System (ADS)

Ding, L.; Li, H.; Hu, C.; Zhang, W.; Wang, S.

2018-04-01

In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

Multi-source and multi-angle remote sensing image data collection, application and sharing of Beichuan National Earthquake Ruins Museum

NASA Astrophysics Data System (ADS)

Lin, Yueguan; Wang, Wei; Wen, Qi; Huang, He; Lin, Jingli; Zhang, Wei

2015-12-01

Ms8.0 Wenchuan earthquake that occurred on May 12, 2008 brought huge casualties and property losses to the Chinese people, and Beichuan County was destroyed in the earthquake. In order to leave a site for commemorate of the people, and for science propaganda and research of earthquake science, Beichuan National Earthquake Ruins Museum has been built on the ruins of Beichuan county. Based on the demand for digital preservation of the earthquake ruins park and collection of earthquake damage assessment of research and data needs, we set up a data set of Beichuan National Earthquake Ruins Museum, including satellite remote sensing image, airborne remote sensing image, ground photogrammetry data and ground acquisition data. At the same time, in order to make a better service for earthquake science research, we design the sharing ideas and schemes for this scientific data set.

Remote imagery for unmanned ground vehicles: the future of path planning for ground robotics

NASA Astrophysics Data System (ADS)

Frederick, Philip A.; Theisen, Bernard L.; Ward, Derek

2006-10-01

Remote Imagery for Unmanned Ground Vehicles (RIUGV) uses a combination of high-resolution multi-spectral satellite imagery and advanced commercial off-the-self (COTS) object-oriented image processing software to provide automated terrain feature extraction and classification. This information, along with elevation data, infrared imagery, a vehicle mobility model and various meta-data (local weather reports, Zobler Soil map, etc...), is fed into automated path planning software to provide a stand-alone ability to generate rapidly updateable dynamic mobility maps for Manned or Unmanned Ground Vehicles (MGVs or UGVs). These polygon based mobility maps can reside on an individual platform or a tactical network. When new information is available, change files are generated and ingested into existing mobility maps based on user selected criteria. Bandwidth concerns are mitigated by the use of shape files for the representation of the data (e.g. each object in the scene is represented by a shape file and thus can be transmitted individually). User input (desired level of stealth, required time of arrival, etc...) determines the priority in which objects are tagged for updates. This paper will also discuss the planned July 2006 field experiment.

Roadside versus remote: Is the habitat sampled by the Breeding Bird Survey in Maryland representative of statewide conditions?

USGS Publications Warehouse

Boone, D.D.; Sauer, J.R.; Thomas, I.; Handley, Lawrence R.; D'Erchia, Frank J.; Charron, Tammy M.

2000-01-01

The North American Breeding Bird Survey (BBS) has received criticism that the bird habitat sampled along the 24.5 mile long roadside transects may not be proportional to regional totals. If true, trends in bird populations recorded by the BBS may not be sensitive predictors of regional or continental change in songbird abundance. To test whether the approximately 60 BBS routes in Maryland representatively sample the state's habitat, a geographic information system (GIS) database was compiled of significant bird habitat identified from remotely sensed landcover and land-use information (e.g., Multi-Resolution Land Characteristics Consortiumclassified Landsat Thematic Mapper imagery, etc.). These GIS data layers were analyzed to determine the statewide acreage of identified habitats as well as the acreage in each of the major physiographic regions of Maryland. Regional and statewide totals were also extracted for the subsample of habitat within 30 m of the BBS transects. The results of the comparison of regional and statewide habitat totals with the BBS sample showed very low proportional difference for nearly all of the identified habitat parameters. For Maryland and perhaps other urbanizing states, the BBS provides an accurate sample of available songbird habitats.

Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users

PubMed Central

Calera, Alfonso; Campos, Isidro; Osann, Anna; D’Urso, Guido; Menenti, Massimo

2017-01-01

The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution. This information is greatly appreciated by the end users, i.e., professional farmers or decision-makers, and can be provided in an easy-to-use manner and in near-real-time by using the improvements achieved in web-GIS methodologies (Geographic Information Systems based on web technologies). This paper reviews the most operational and explored methods based on optical remote sensing for the assessment of crop water requirements, identifying strengths and weaknesses and proposing alternatives to advance towards full operational application of this methodology. In addition, we provide a general overview of the tools, which facilitates co-creation and collaboration with stakeholders, paying special attention to these approaches based on web-GIS tools. PMID:28492515