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  Subjects -> ELECTRONICS (Total: 152 journals)
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 1)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 5)
Advances in Microelectronic Engineering     Open Access   (Followers: 2)
Advances in Power Electronics     Open Access   (Followers: 8)
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 68)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 10)
Annals of Telecommunications     Hybrid Journal   (Followers: 4)
APL : Organic Electronics and Photonics     Hybrid Journal   (Followers: 2)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 6)
Archives of Electrical Engineering     Open Access   (Followers: 9)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 5)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 9)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 16)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 14)
Biomedical Instrumentation & Technology     Full-text available via subscription   (Followers: 4)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 5)
BULLETIN of National Technical University of Ukraine. Series RADIOTECHNIQUE. RADIOAPPARATUS BUILDING     Open Access   (Followers: 2)
Bulletin of the Polish Academy of Sciences : Technical Sciences     Open Access  
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 13)
China Communications     Full-text available via subscription   (Followers: 4)
Circuits and Systems     Open Access   (Followers: 9)
Consumer Electronics Times     Open Access   (Followers: 4)
Control Systems     Hybrid Journal   (Followers: 22)
Electronic Markets     Hybrid Journal   (Followers: 5)
Electronic Materials Letters     Hybrid Journal   (Followers: 3)
Electronics     Open Access   (Followers: 7)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 5)
Electronics Letters     Hybrid Journal   (Followers: 19)
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 20)
Energy Harvesting and Systems : Materials, Mechanisms, Circuits and Storage     Hybrid Journal   (Followers: 1)
EPJ Quantum Technology     Open Access  
EURASIP Journal on Embedded Systems     Open Access   (Followers: 8)
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Foundations and TrendsĀ® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Foundations and TrendsĀ® in Signal Processing     Full-text available via subscription   (Followers: 4)
Frequenz     Full-text available via subscription   (Followers: 2)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 2)
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (Followers: 21)
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 4)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 15)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 14)
IEEE Consumer Electronics Magazine     Full-text available via subscription   (Followers: 16)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 10)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 2)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 6)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 10)
IEEE Transactions on Audio, Speech, and Language Processing     Hybrid Journal   (Followers: 12)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 23)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 16)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 7)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 14)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 17)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 8)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 7)
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 7)
IET Power Electronics     Hybrid Journal   (Followers: 10)
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 7)
IETE Journal of Education     Open Access   (Followers: 2)
IETE Journal of Research     Open Access   (Followers: 9)
IETE Technical Review     Open Access   (Followers: 4)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 12)
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 5)
Informatik-Spektrum     Hybrid Journal  
Instabilities in Silicon Devices     Full-text available via subscription  
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 1)
International Journal of Advanced Electronics and Communication Systems     Open Access   (Followers: 5)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 20)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 3)
International Journal of Aerospace Innovations     Full-text available via subscription   (Followers: 11)
International Journal of Antennas and Propagation     Open Access   (Followers: 8)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 1)
International Journal of Biomedical Nanoscience and Nanotechnology     Hybrid Journal   (Followers: 7)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 4)
International Journal of Computer & Electronics Research     Full-text available via subscription   (Followers: 2)
International Journal of Control     Hybrid Journal   (Followers: 12)
International Journal of Electronics     Hybrid Journal   (Followers: 2)
International Journal of Electronics & Data Communication     Open Access   (Followers: 4)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 3)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 1)
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 1)
International Journal of Nano Devices, Sensors and Systems     Open Access   (Followers: 4)
International Journal of Nanoscience     Hybrid Journal   (Followers: 1)
International Journal of Numerical Modelling:Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 2)
International Journal of Power Electronics     Hybrid Journal   (Followers: 5)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 2)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 2)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 2)
International Journal on Communication     Full-text available via subscription   (Followers: 8)
International Journal on Electrical and Power Engineering     Full-text available via subscription   (Followers: 11)
International Transaction of Electrical and Computer Engineers System     Open Access  
ISRN Electronics     Open Access   (Followers: 1)
ISRN Signal Processing     Open Access   (Followers: 1)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 5)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 1)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 2)
Journal of Electrical Bioimpedance     Full-text available via subscription   (Followers: 2)
Journal of Electrical Engineering & Electronic Technology     Full-text available via subscription   (Followers: 1)
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 5)

        1 2 | Last

  Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
  [SJR: 1.232]   [H-I: 14]   [19 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1939-1404
   Published by Institute of Electrical and Electronics Engineers (IEEE) Homepage  [176 journals]
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote
           Sensing Information for Authors
    • Pages: C3 - C3
      Abstract: Provides instructions and guidelines to prospective authors who wish to submit manuscripts.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • IEEE Transactions on Geoscience and Remote Sensing institutional listings
    • Pages: C4 - C4
      Abstract: Advertisment.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • [Front cover]
    • Pages: C1 - C1
      Abstract: Presents the front cover for this issue of the publication.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote
           Sensing publication information
    • Pages: C2 - C2
      Abstract: Provides a listing of current staff, committee members and society officers.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Table of contents
    • Pages: 437 - 438
      Abstract: Presents the table of contents for this issue of this publication.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Validating GEOV1 Fractional Vegetation Cover Derived From
           Coarse-Resolution Remote Sensing Images Over Croplands
    • Authors: Mu; X.;Huang, S.;Ren, H.;Yan, G.;Song, W.;Ruan, G.;
      Pages: 439 - 446
      Abstract: Fractional vegetation cover (FVC) is one of the most important criteria for surface vegetation status. This criterion corresponds to the complement of gap fraction unity at the nadir direction and accounts for the amount of horizontal vegetation distribution. This study aims to directly validate the accuracy of FVC products over crops at coarse resolutions (1 km) by employing field measurements and high-resolution data. The study area was within an oasis in the Heihe Basin, Northwest China, where the Heihe Watershed Allied Telemetry Experimental Research was conducted. Reference FVC was generated through upscaling, which fitted field-measured data with spaceborne and airborne data to retrieve high-resolution FVC, and then high-resolution FVC was aggregated with a coarse scale. The fraction of green vegetation cover product (i.e., GEOV1 FVC) of SPOT/VEGETATION data taken during the GEOLAND2 project was compared with reference data. GEOV1 FVC was generally overestimated for crops in the study area compared with our estimates. Reference FVC exhibits a systematic uncertainty, and GEOV1 can overestimate FVC by up to 0.20. This finding indicates the necessity of reanalyzing and improving GEOV1 FVC over croplands.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • An Improved Particle Filter Algorithm Based on Ensemble Kalman Filter and
           Markov Chain Monte Carlo Method
    • Authors: Bi; H.;Ma, J.;Wang, F.;
      Pages: 447 - 459
      Abstract: Data assimilation (DA) has developed into an important method in Earth science research due to its capability of combining model dynamics and observations. Among various DA methods, the particle filter (PF) is free from the constraints of linear models and Gaussian error distributions. Thus, it is now receiving increasing attention in DA. However, the particle degeneracy still remains a major problem in practical application of PF. In this paper, an improved PF is proposed based on ensemble Kalman filter (EnKF) and the Markov Chain Monte Carlo (MCMC) method. It uses an EnKF analysis to define the proposal density of PF instead of the prior density, thus reducing the risk of particle degeneracy. Furthermore, when particle degeneracy happens, resampling is performed follow by an MCMC move step to increase the diversity of particles, thus reducing the potential of particle impoverishment and improving the accuracy of the filter. Finally, the improved PF is tested by assimilating brightness temperatures from the Advanced Microwave Scanning Radiometer (AMSR-E) into the variance infiltration capacity (VIC) model to estimate soil moisture in the NaQu network region at the Tibetan Plateau. The experiment results show that the improved PF can provide more accurate assimilation results and also need fewer particles to get reliable estimations than the EnKF and the standard PF, thus demonstrating the effectiveness and practicality of the improved PF.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • A Preliminary Study of the Calibration for the Rotating Fan-Beam
           Scatterometer on CFOSAT
    • Authors: Zhu; J.;Dong, X.;Lin, W.;Zhu, D.;
      Pages: 460 - 470
      Abstract: The first rotating fan-beam scatterometer (RFSCAT) will be launched onboard the Chinese–French Oceanic Satellite (CFOSAT) in 2018. It provides a set of radar cross-section ( $boldsymbol{sigma^{0}}$ ) measurements at different azimuth/incidence angles over a wind vector cell (WVC), in order to determine the near-surface wind field using the backscatter model, i.e., the so-called geophysical model function (GMF). The accuracy of the retrieved wind vector is a sensitive function of the radiometric accuracy of the $boldsymbol{sigma^{0}}$ measurements. Therefore, in-flight calibration, including the loop-back (internal) calibration and the external calibration performed with natural extended-area targets, is studied in this paper. Several homogeneous areas over land are first analyzed to check the stability and azimuthal dependence of the $boldsymbol{sigma^{0}}$ over these areas. A new calibration mask of the homogeneous land areas is generated and will be used by RFSCAT calibration. Then a simple method of external calibration is proposed to eliminate the azimuthal-dependent $boldsymbol{sigma^{0}}$ errors induced by the insertion loss of the rotating joint, which can be applied to both the rotating pencil-beam scatterometers and the coming RFSCAT. The “observed” $boldsymbol{sigma^{0}}$ of RFSCAT is simulated using the Seasat-A scatterometer (SASS) measurements and the “perturbed” azimuthal-dependent $boldsymbol{sigma^{0}}$ errors. The latter is then tracked by the proposed external calibration. The res- lts show that the accuracy of gain corrections is up to 0.2 dB, ensuring consistency between different azimuthal measurements.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Optimizing Remote Sensing-Based Level–Area Modeling of Large Lake
           Wetlands: Case Study of Poyang Lake
    • Authors: Cai; X.;Gan, W.;Ji, W.;Zhao, X.;Wang, X.;Chen, X.;
      Pages: 471 - 479
      Abstract: Remote sensing-derived level–area models have been widely used in inundation analysis of large lakes. The current study aimed to optimize the model for Poyang Lake, the largest freshwater lake in China, where the hydrological connections are highly dynamic and complex. The inundation data delineated using 217 MODIS images between 2003 and 2005 together with concurrent water level data were used to analyze the level–area model accuracy and its associated influential factors. It has been demonstrated that the primary model uncertainty was introduced by the image selection in terms of both magnitude and temporal distribution. The results from random sampling simulations indicate that at least 40 remotely sensed images are required to assure a stable linear regression model. In addition, the selection of gauging stations, where the water level measurements were collected, could serve as another error source to the model. If the model input (water level) changes between different gauging stations, the variability of the output (inundation area) could reach to ${bf{ 144.49}}; {bf{ km}}^{{bf{ 2}}}$ . Moreover, the model performance could be improved through the matched regression functions, where the average improvement among different regression functions is ${bf{ 134.44}}; {bf{ km}}^{{bf{ 2}}}$ . Of the 40 selected models, the logistic regression based on the lake’s inundation patterns appears to be the best, resulting in an ${boldsymbol R}^{bf{ 2}}$ of 0.98 and uncertainty of ${bf{ 100.45}}; {bf{ km}}^{{bf{ 2}}}$ . This report describes the first attempt in which the logistic function has been used in level–area models development.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • The Nature of Fire Ash Particles: Microwave Material Properties, Dynamic
           Behavior, and Temperature Correlation
    • Authors: Baum; T.C.;Thompson, L.;Ghorbani, K.;
      Pages: 480 - 492
      Abstract: This paper focuses on the investigation of a number of physical and electromagnetic properties of fire generated ash particles, with relation to radar observations of forest fire smoke columns. Emphasis is placed on understanding the physical properties of the ash, which have direct effects on their scattering ability. Coupled with the electromagnetic properties, these physical properties describe the scatter generated when a number of dispersed ash particles are volumetrically interacting with radar signals. Due to their planar geometry, a study of ash particles originating from the eucalyptus genus has been conducted. Particular focus is placed on this genus due to its high population and role in fueling large bushfires within the Australian continent. The fundamental scattering mechanisms required for describing the radar reflectivity in horizontal, vertical, and cross-polarization have been explored by breaking down and analyzing three distinct properties of an individual ash particle. These include its geometric, dynamic, and electromagnetic properties. Statistical distributions from all three areas have been included to aid in the development of modeling tools.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Effects of Soil Surface Irregularities on the Diurnal Variation of Soil
           Broadband Blue-Sky Albedo
    • Authors: Cierniewski; J.;Karnieli, A.;Kazmierowski, C.;Krolewicz, S.;Piekarczyk, J.;Lewinska, K.;Goldberg, A.;Wesolowski, R.;Orzechowski, M.;
      Pages: 493 - 502
      Abstract: This paper quantitatively explores, in terms of roughness indices, the effect of soil surface irregularities on the diurnal variation of the broadband blue-sky albedo of a large range of soil properties. Field studies were carried out on cultivated and uncultivated soil surfaces in Poland and Israel that vary in roughness and brightness. It was found that these irregularities, formed by different agricultural equipment and modified by rain or sprinkler irrigation, can be quantified by two roughness indices. Soil roughness not only affects the overall level of the diurnal variation of the albedo, but also affects the intensity of the diurnal increase from the solar zenith angle ( ${thetab_{mbi s}}$ ) at the local noon to about $75^circ - 80^circ$ . The roughness indices are variables that precisely determine only the albedo at the local solar noon of soils with the same color value. If the contents of soil organic carbon (SOC) and calcium carbonate are treated as the dominant variables, combined with one of the indices, these three variables together would significantly describe the albedo at the local solar noon of all soil surfaces. The soils, with their high irregularities, showed almost no rising values of albedo at a ${thetab_{mbi s}}$ lower than 75 $^circ$ , while the smooth soil surfaces exhibited a gradual increase of the albedo at these angles. It is concluded that the roughness indices provide sufficient means to accurately describe the diurnal variation of the albedo of a wide range of surfaces, disregarding other soil properties.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Mangrove Mapping and Change Detection in Ca Mau Peninsula, Vietnam, Using
           Landsat Data and Object-Based Image Analysis
    • Authors: Son; N.;Chen, C.;Chang, N.;Chen, C.;Chang, L.;Thanh, B.;
      Pages: 503 - 510
      Abstract: Mangrove forests provide important ecosystem goods and services for human society. Extensive coastal development in many developing countries has converted mangrove forests to other land uses without regard to their ecosystem service values; thus, the ecosystem state of mangrove forests is critical for officials to evaluate sustainable coastal management strategies. The objective of this study is to investigate the multidecadal change in mangrove forests in Ca Mau peninsula, South Vietnam, based on Landsat data from 1979 to 2013. The data were processed through four main steps: 1) data preprocessing; 2) image processing using the object-based image analysis (OBIA); 3) accuracy assessment; and 4) multitemporal change detection and spatial analysis of mangrove forests. The classification maps compared with the ground reference data showed the satisfactory agreement with the overall accuracy higher than 82%. From 1979 to 2013, the area of mangrove forests in the study region had decreased by 74%, mainly due to the boom of local aquaculture industry in the study region. Given that mangrove reforestation and afforestation only contributed about 13.2% during the last three decades, advanced mangrove management strategies are in an acute need for promoting environmental sustainability in the future.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Joint Use of ICESat/GLAS and Landsat Data in Land Cover Classification: A
           Case Study in Henan Province, China
    • Authors: Liu; C.;Huang, H.;Gong, P.;Wang, X.;Wang, J.;Li, W.;Li, C.;Li, Z.;
      Pages: 511 - 522
      Abstract: Lidar waveform features from the Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System (ICESat/GLAS) and spectral features from Landsat Thematic Mapper (TM)/Enhanced TM Plus (ETM+) were used to discriminate land cover categories for GLAS footprints in Henan Province, China. Fifteen waveform metrics were derived from GLAS data while band ratios and surface spectral reflectance were taken from Landsat TM/ETM+. Random forest (RF) was used in feature selection and classification of footprints along with support vector machines (SVMs). The categories of classification included croplands, forests, shrublands, water bodies, and impervious surfaces. Compared with the use of waveform or spectral features alone in land cover classification, the joint use of waveform and spectral data as inputs improved the classification accuracy of footprints. An overall accuracy (OA) of 91% was achieved by either RF or SVM when features from both GLAS and Landsat sources were used increasing upon an accuracy of 85% if only one source was used. The high accuracy land cover data obtained by the joint use of the two data sources could be used as additional references in large scale land cover mapping when ground truth is hard to obtain. It is believed that the increase in accuracy is largely a result from the inclusion of the additional information of vertical structure offered by waveform lidar.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Understanding the Geophysical Sources of Uncertainty for Satellite
           Interferometric (SRTM)-Based Discharge Estimation in River Deltas: The
           Case for Bangladesh
    • Authors: Sikder; M.S.;Hossain, F.;
      Pages: 523 - 538
      Abstract: Like most river deltas, Bangladesh represents a geographically small region with numerous crisscrossing rivers. The total number of rivers in Bangladesh exceeds 300, of which 57 rivers are transboundary. Given the widespread unavailability of flow data across the entire river basins of Ganges, Brahmaputra, and Meghna, combined with a declining measurement network and political challenges of sharing the data, satellite remote sensing of discharge has recently become a viable alternative. This study was motivated by the need to understand the geophysical sources of uncertainty of satellite interferometric-based discharge estimation in Bangladesh. A consequential goal of this study was to contextualize the understanding as a function of river’s geophysical characteristics (river width, reach averaging length, and bed/water slope) and also to explore a pragmatic approach to uncertainty reduction using water level climatology. Discharge was estimated according to the slope-area (Manning’s) method using elevation data from Shuttle Radar Topography Mission (SRTM). A high-resolution hydrodynamic (HD) model was accurately calibrated to simulate water level and flow dynamics along the river reaches of the river network and serve as reference for comparison with satellite-based estimates. It was found that satellite interferometric (SRTM)-based discharge estimates yielded estimation error variance an order smaller than the natural flow variability only if the river width was at least three times larger the width of the native resolution of satellite elevation data. Rivers narrower than this width (for SRTM, this cutoff is 270 m) yielded a coefficient of variation larger than 1 due to contamination of land elevation data in hydraulic parameter calculations. It was also found that water level climatology can be useful in significantly reducing the estimation uncertainty for these narrow rivers. While reach averaging length appeared insensitive to accurac- for wide rivers (width ${bf gt! 1nbsphbox{km}}$ ), a few rivers seemed to have an optimal reach averaging length at which the highest accuracy is obtained. Finally, it was found that if reach-averaged hydraulic parameters (area, slope, and radius) are used for the calculation of reach-averaged discharge, the needed linear (bias) correction factors, although unique and arbitrary for each river reach, can improve accuracy of flow simulations.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Remote Sensing Observations on Impact of Phailin Cyclone on Phytoplankton
           Distribution in Northern Bay of Bengal
    • Authors: Sarangi; R.K.;Mishra, M.K.;Chauhan, P.;
      Pages: 539 - 549
      Abstract: Remote sensing-based analysis has been carried out to study the impact of “Phailin” cyclone on ocean phytoplankton distribution off Odisha coast and on the northern Bay of Bengal water. Oceansat-2 Ocean Colour Monitor (OCM) and MODIS-Terra sensors-derived chlorophyll images have been generated over the study area during October 2013. There has been observation of drastic change in the chlorophyll concentration of the north-western Bay of Bengal water with effect of the cyclone “Phailin,” which hit Gopalpur, Odisha coast on 12 October night at around 21:00 h IST. The postcyclone images of Oceansat-2 OCM data and retrieved chlorophyll concentration were observed to be very high, ${bf{3.0 - 4.0}};{bf{mg/m}}^{bf{3}}$ in coastal water, which was around ${bf{0.5 - 1.0}};{bf{mg/m}}^{bf{3}}$ during precyclone. Similarly, the postcyclone chlorophyll was around ${bf{0.80 - 1.50}};{bf{mg/m}}^{bf{3}}$ in the offshore water, unusually high compared to precyclone concentration ( $< {bf{0.60}};{bf{mg/m}}^{bf{3}}$ ). The cyclone track of India Meteorological Department (IMD) has been superimposed on chlorophyll images and observed as proxy along the chlorophyll front with algal bloom features. There has been observation of cooling in the northern Bay of Bengal and off Odisha coast with impact of the induced upwelling, entrainment and mixing of the water column, as evident from the sea surface temperature (SST) data analysis. Interesting features such as cold core eddies and fronts have been visualized. This study is important to assess the impact of Phailin cyclone on ocean productivity on the local and basin scale and its deviation from seasona- trend as well.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Estimating the Aboveground Dry Biomass of Grass by Assimilation of
           Retrieved LAI Into a Crop Growth Model
    • Authors: He; B.;Li, X.;Quan, X.;Qiu, S.;
      Pages: 550 - 561
      Abstract: This study presents a method to assimilate leaf area index (LAI) retrieved from MODIS data using a physically based method into a soil–water–atmosphere–plant (SWAP) model to estimate the aboveground dry biomass of grass in the Ruoergai grassland, China. The assimilation method consists of reinitializing the model with optimal input parameters that allow a better temporal agreement between the LAI simulated by the SWAP model and the LAI retrieved from MODIS data. The minimization is performed by a four-dimensional variational data assimilation (4D-VAR) algorithm but which is challenged by the development of the adjoint model. The automatic differentiation (AD) technique is thus used to provide the adjoint model at the level of computer language codes. After the re-initialization, the simulated aboveground dry biomass value is compared with ground measurements taken in early August 2013. The results show that the biomass can be estimated with highly satisfactory accuracy level through the assimilation method with ${bf{R}}^2(bf{the,deterministic,coefficient}) = {bf{0.73}}$ and ${bf{RMSE}}(bf{root-mean-square,error}) = {bf{617.94}};bf{kg,ha}^{ -1}$ . The accuracy is further improved when the newly derived ${bf{RMSE}}_{{bf{LAI}}}$ values are used as observation errors in the assimilation process, with ${bf{R}}^2 = {bf{0.76}}$ and ${bf{RMSE}} = {bf{542.52}};bf{kg,ha}^{-1}$ . Both assimilation strategies yield a significant improvement in SWAP model accuracy with respect to no significant correlation obtained when the SWAP model is run alone with constant values of the input parame- ers employed for the whole area. The validity of the 4D-VAR method for biomass estimation is well demonstrated.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Issues and Potential Improvement of Multiband Models for Remotely
           Estimating Chlorophyll-a in Complex Inland Waters
    • Authors: Zhu; W.;Yu, Q.;Tian, Y.Q.;Becker, B.L.;Carrick, H.;
      Pages: 562 - 575
      Abstract: Remote estimation of chlorophyll-a (chl-a) in complex freshwaters remains a challenging problem due to the rapid spatial variability and wide range as influenced by terrestrial constituents. A controversial issue is whether or not 2-B models possess sufficient wavelength information for accurately estimating Chl-a concentrations from remote sensing data for freshwater environments. This study introduced a systemic approach and proved that adding additional wavelength information to 2-B model could not significantly improve the estimation of freshwater chl-a, but acted to increase model uncertainty. This convincing solution was based on a large synthetic data set (38 937 samples) combined with a set of in situ data (51 samples) collected in three cruises in Lake Huron. The synthetic data set has two distinct features: 1) large data items and 2) covers a broad range of chl-a ( ${bf 0 - 1000};{bf {mg}}/{{bf m}^{bf 3}}$ ), colored dissolved organic matter (CDOM) ( ${bf 0 - 50};{bf m}^{bf - 1}$ ), and NAP (nonalgal particles) (0–500 mg/l). Additionally, this study reveals how hyperspectral wavelength selection, number of bands, bandwidth, and parameter calibration are associated with the uncertainty in remote sensing of chl-a. The systematic analysis approach was used to evaluate 34 chl-a algorithms by using optimal location and number of wavelengths as well as calibrated parameters. The study introduced a set of new 2-B, 3-B, and 4-B models derived also from using optimized parameters, suggested wavelengths, and bands available in MERIS and MODIS satellite images. Validation results demonstrated that these models are suitable to general freshwater environments because of broad ranges of biochemical and physical properties in both synthetic and in situ data.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Multisource Data Fusion and Fisher Criterion-Based Nearest Feature Space
           Approach to Landslide Classification
    • Authors: Chang; Y.;Wang, Y.C.;Fu, Y.;Han, C.;Chanussot, J.;Huang, B.;
      Pages: 576 - 588
      Abstract: In this paper, a novel technique known as the Fisher criterion-based nearest feature space (FCNFS) approach is proposed for supervised classification of multisource images for the purpose of landslide hazard assessment. The method is developed for land cover classification based upon the fusion of remotely sensed images of the same scene collected from multiple sources. This paper presents a framework for data fusion of multisource remotely sensed images, consisting of two approaches: 1) the band generation process(BGP); and 2) the FCNFS classifier. We propose the BGP to create a new set of additional bands that are specifically accommodated to the landslide class and are extracted from the original multisource images. In comparison to the original nearest feature space (NFS) method, the proposed improved FCNFS classifier uses the Fisher criterion of between-class and within-class discrimination to enhance the classifier. In the training phase, the labeled samples are discriminated by the Fisher criterion, which can be treated as a preprocessing step of the NFS method. After completion of the training, the classification results can be obtained from the NFS algorithm. In order for the proposed FCNFS to be effective for multispectral images, a multiple adaptive BGP is introduced to create an additional set of bands specially accommodated to landslide classes. Experimental results show that the proposed BGP/FCNFS framework is suitable for land cover classification in Earth remote sensing and improves the classification accuracy compared to conventional classifiers.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Refinement of SMOS Multiangular Brightness Temperature Toward Soil
           Moisture Retrieval and Its Analysis Over Reference Targets
    • Authors: Zhao; T.;Shi, J.;Bindlish, R.;Jackson, T.J.;Kerr, Y.H.;Cosh, M.H.;Cui, Q.;Li, Y.;Xiong, C.;Che, T.;
      Pages: 589 - 603
      Abstract: Soil moisture ocean salinity (SMOS) mission has been providing L-band multiangular brightness temperature observations at a global scale since its launch in November 2009 and has performed well in the retrieval of soil moisture. The multiple incidence angle observations also allow for the retrieval of additional parameters beyond soil moisture, but these are not obtained at fixed values and the resolution and accuracy change with the grid locations over SMOS snapshot images. Radio-frequency interference (RFI) issues and aliasing at lower look angles increase the uncertainty of observations and thereby affect the soil moisture retrieval that utilizes observations at specific angles. In this study, we proposed a two-step regression approach that uses a mixed objective function based on SMOS L1c data products to refine characteristics of multiangular observations. The approach was found to be robust by validation using simulations from a radiative transfer model, and valuable in improving soil moisture estimates from SMOS. In addition, refined brightness temperatures were analyzed over three external targets: Antarctic ice sheet, Amazon rainforest, and Sahara desert, by comparing with WindSat observations. These results provide insights for selecting and utilizing external targets as part of the upcoming soil moisture active passive (SMAP) mission.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • An Approach for Monitoring Global Vegetation Based on Multiangular
           Observations From SMOS
    • Authors: Cui; Q.;Shi, J.;Du, J.;Zhao, T.;Xiong, C.;
      Pages: 604 - 616
      Abstract: Vegetation monitoring is important for the study of the global carbon cycle and ecosystem. The soil moisture and ocean salinity (SMOS) mission that launched in 2009 is the first operational L-band passive microwave spaceborne sensor using synthetic aperture techniques; the sensor provides global L-band multiangular observations. In this study, based on the commonly used zero-order radiative transfer model ( $omega - tau $ model), we developed an approach for retrieving vegetation optical depth (VOD) using only SMOS H-polarized multiangular measurements. This was done by minimizing the soil signal and separating the vegetation signal from the multiangular brightness temperature. The uniqueness of this approach is that the angular feature of soil emission is used and that the VOD is retrieved directly from the H-polarized multiangular brightness temperature without any field correction or auxiliary soil or vegetation data. This approach is first validated by theoretical modeling and experimental data. The results demonstrate that VOD can be reliably estimated using this approach. The retrieved VOD is then compared with aboveground biomass, which shows strong correlation. Global mean VOD for the years 2010–2011 generally shows a clear global pattern and corresponds well to the land cover types. The VOD of nine representative regions that are homogeneously covered with different vegetation types from 2010 to 2011 is compared with normalized difference vegetation index (NDVI). The results indicate that the VOD can generally reveal vegetation seasonal changes and can provide unique information for vegetation monitoring.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Localization of RFI Sources for the SMOS Mission: A Means for Assessing
           SMOS Pointing Performances
    • Authors: Soldo; Y.;Cabot, F.;Khazaal, A.;Miernecki, M.;Slominska, E.;Fieuzal, R.;Kerr, Y.H.;
      Pages: 617 - 627
      Abstract: Artificial sources emitting in the protected part of the L-band are contaminating the retrievals of the soil moisture and ocean salinity (SMOS) satellite launched by the European Space Agency (ESA) in November 2009. Detecting and pinpointing such sources is crucial for the improvement of SMOS science products as well as for the identification of the emitters. In this contribution, we present a method to obtain snapshot-wise information about sources of radio-frequency interference (RFI). The localization accuracy of this method is also assessed for observed RFI sources. We also show that RFI localizations constitute a useful data set for assessing the pointing performance of the satellite, and present how it is possible, using the results of this method, to identify and estimate two systematic errors in the geo-location of the satellite field of view. The potential causes and the approaches to mitigate both these errors are discussed.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • A New Model for Surface Soil Moisture Retrieval From CBERS-02B Satellite
           Imagery
    • Authors: Zhou; G.;Tao, X.;Sun, Y.;Zhang, R.;Yue, T.;Yang, B.;
      Pages: 628 - 637
      Abstract: This paper develops a new model for surface soil moisture (SSM) retrieval from CBERS-02B images. The paper first analyzes the existing SSM retrieval model from Landsat TM imagery and establishes the spectral radiance relationship of each band between Landsat TM and CBERS-02B. The model associated parameters including mean reflectance, mean atmospheric transmittance, and mean sun radial brightness of each band between Landsat TM and CBERS-02B is established. The model is finally adjusted by considering the differences of response frequency and sensitivity in the two satellite sensors. Two test areas, Jili Village of Laibin county, Guangxi Province, China and Yuanjiaduan Village of Jiujiang County, JiangXi Province, China are chosen to verify the correctness of the developed model. The SSMs retrieved from Landsat TM imagery are chosen as references. The accuracy of the proposed model is evaluated through correlation coefficient and root-mean-square error (RMSE) relative to the SSMs retrieved from Landsat TM images. The verified results discover that the relative accuracy of the average SSMs retrieved by the proposed model from CBERS-02B can reach over 91.0% when compared to the SSMs retrieved from Lansat TM. In addition, six types of lands are used to further evaluate the accuracy of the proposed model. The experimental results in two areas show that the correlation coefficient and the RMSE between two SSMs from CBERS-02B and Landsat TM achieves over 0.9 and 0.011 ( ${bf{m}^3}/{bf{m}^3}$ ), respectively, in both rocky desertification land and dry land; achieve over 0.81 and 0.09 ( ${bf{m}^3}/{bf{m}^3}$ ), respectively, in rice field, shrub land, and woodland. These results demonstrate that the model developed in this paper can effectively calculate the SSMs for CBERS-02B satellite imagery.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • A Simple Method for Soil Moisture Determination From LST–VI Feature
           Space Using Nonlinear Interpolation Based on Thermal Infrared Remotely
           Sensed Data
    • Authors: Zhang; D.;Tang, R.;Tang, B.;Wu, H.;Li, Z.;
      Pages: 638 - 648
      Abstract: Soil moisture is an important parameter that is widely used for drought monitoring and characterizing environment. Based on the universal triangular space constructed by the normalized land surface temperature (LST) and normalized difference vegetation index (NDVI), this study proposes a simple method to estimate surface soil moisture (SSM) using a nonlinear interpolation function with theoretical limiting edges derived from the energy balance principle. As an interpolation function, the quadratic polynomial equation is used to reveal the relationships among soil moisture, LST, and fractional vegetation cover (FVC), which are determined and validated by the simulation data from the Noah land surface model (Noah LSM). The developed function is much simpler than Carlson’s function because the number of coefficients is reduced by half. The proposed method is applied to the North China Plain. The estimation results are also validated by the in situ measurements of soil water content at 10 and 20 cm depths at the Yucheng meteorological station; the corresponding biases are as high as 0.02 and $0.12;{{rm m}^{3}}/{{rm m}^{3}}$ , respectively, and the root-mean-square errors (RMSEs) are approximately 0.04 and $0.10;{{rm m}^3}/{{rm m}^3}$ , respectively. The results indicate that this method has high-estimation accuracy and overcomes the weakness induced by the effects of atmospheric conditions on different days. This study provides insight into the comparable soil moisture estimation from remotely sensed data on a regional scale.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Detecting Seismic Anomalies in Outgoing Long-Wave Radiation Data
    • Authors: Kong; X.;Bi, Y.;Glass, D.H.;
      Pages: 649 - 660
      Abstract: In this paper, we propose a Geometric Moving Average Martingale (GMAM) method for change detection. There are two components underpinning the method which enable it to reduce false detections. The first is the exponential weighting of observations to obtain the GMAM value and the second is the use of the value for hypothesis testing to determine whether a change has occurred. Extension of the GMAM method to the average GMAM (AG) method has been applied to analyze seismic anomalies within outgoing long-wave radiation (OLR) data observed by satellites from 2006 to 2013 for the two recent Wenchuan and Lushan earthquakes and four comparative study areas: Wenchuan, Puer, Beijing, and Northeastern areas. The Yushu earthquake and Hetian earthquake have also been examined. The experimental results show that the proposed AG method can effectively extract abnormal changes within OLR data and that there are large AG values in the pre and postoccurrence of the earthquakes in these areas, which could be viewed as seismic anomalies, and the AG method has experimentally compared with the deviation method. The experimental results show that the AG method can effectively reflect the change process in OLR data.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Calibration of Aboveground Forest Carbon Stock Models for Major Tropical
           Forests in Central Sumatra Using Airborne LiDAR and Field Measurement Data
           
    • Authors: Thapa; R.B.;Watanabe, M.;Motohka, T.;Shiraishi, T.;Shimada, M.;
      Pages: 661 - 673
      Abstract: Despite substantial policy attention, tropical forests in Southeast Asian region are releasing large amount of carbon to the atmosphere due to accelerating deforestation. Accurately determining forest statistics and characterizing aboveground forest carbon stocks (AFCSs) are always challenging in the region. In order to develop more accurate estimates of AFCS, the present study collected airborne LiDAR and field measurements data and calibrated AFCS models to estimate carbon stock in the tropical forests in central Sumatra. The study region consists of natural forests, including peat swamp, dry moist, regrowth, and mangrove, and plantation forests, including rubber, acacia, oil palm, and coconut. To cover the different forest types, 60 field plots of 1 ha in size were inventoried. Eight transects crossing these field plots were acquired to calibrate the LiDAR to AFCS models. The AFCS values for the field plots ranged from 4 to ${bf 161}nbsphbox{bf Mg ha}^{- {bf 1}}$ . General models were fitted without considering forest types, whereas a specific model was fitted for each specific forest type. Five alternative general models with different LiDAR metrics were calibrated with model performance expressed as ${{bf R}^{bf 2}}$ ranging from 0.73 to 0.87 and root-mean-square error (RMSE) values ranging from 17.4 to ${bf 25}.{bf 0}nbsphbox{bf Mg ha}^{- {bf 1}}$ . Seven forest-specific AFCS models were calibrated for different forest types, with ${{bf R}^{bf 2}}$ values ranging from 0.72 to 0.97 and RMSE values ranging from 1.4 to ${bf 10}.{bf 7}nbsp{hbox{bf Mg ha}}^{ - {bf 1}}$ . The performance of each model- was cross-validated by iteratively removing one data point. While forest-specific models provide better AFCS estimates, the general models are still useful when forest types are ambiguous.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Manifestation of LiDAR-Derived Parameters in the Spatial Prediction of
           Landslides Using Novel Ensemble Evidential Belief Functions and Support
           Vector Machine Models in GIS
    • Authors: Jebur; M.N.;Pradhan, B.;Tehrany, M.S.;
      Pages: 674 - 690
      Abstract: Landslide susceptibility mapping is indispensable for disaster management and planning development operations in mountainous regions. The potential use of light detection and ranging (LiDAR) data was explored in this study for deriving landslide-conditioning factors for the spatial prediction of landslide-susceptible areas in a landslide-prone area in Ulu Klang, Malaysia. Nine landslide-conditioning factors, such as altitude, slope, aspect, curvature, stream power index (SPI), topographic wetness index (TWI), terrain roughness index (TRI), sediment transport index (STI), and slope length (SL), were directly derived from LiDAR for landslide-susceptibility mapping. The main objective of this research was to propose a novel ensemble landslide susceptibility mapping method to enhance the performance of individual methods of support vector machine (SVM) and evidential belief function (EBF). SVM is time-consuming when various data types, such as nominal, scale, and ordinal, are used. This characteristic of the individual SVM method is not optimal for hazard modeling. This drawback can be resolved by assessing the effects of the classes of each conditioning factor on landslide occurrence through a data-driven EBF method. Hence, EBF was applied in this study, and weights were acquired for the classes of each conditioning factor. The conditioning factors were reclassified based on the attained weights and entered into SVM as a scale to evaluate the correlation between landslide occurrence and each conditioning factor. Four SVM kernel types [radial basis function kernel (RBF), sigmoid kernel (SIG), linear kernel (LN), and polynomial kernel (PL)] were tested to explore the efficiency of each kernel in SVM modeling. The efficiencies of the ensemble EBF and SVM methods were examined through area under curve (AUC). The RBF kernel obtained better results than the other kernel types. The success and prediction rates obtained from the validation results of ensemble EBF and RBF- 13;SVM method were 83.04% and 80.04%, respectively. The proposed novel ensemble method reasonably accelerated the processing and enhanced the results by combining the advantages of both methods.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Three-Dimensional Reconstruction of Large Multilayer Interchange Bridge
           Using Airborne LiDAR Data
    • Authors: CHENG; L.;Wu, Y.;Wang, Y.;Zhong, L.;Chen, Y.;Li, M.;
      Pages: 691 - 708
      Abstract: Accurate and timely updated three-dimensional (3-D) model data of interchange bridges are significant for traffic navigation, landscape design, and many other applications. In this study, we explore the potential of using airborne light detection and ranging (LiDAR) data for 3-D reconstruction of large multilayer interchange bridges. To reduce the technical difficulty in this 3-D reconstruction process, we propose a concept of “structure unit.” We propose a new technical framework based on the structure units for 3-D reconstruction of large multilayer interchange bridge, including point cloud extraction, connectivity-based segmentation, determination of structure units, occlusion detection and restoration, and 3-D modeling. The general idea of this framework is to divide an entire interchange bridge into many simple “structure units,” and then derive a complete 3-D model by reconstructing each “structure unit.” The technical novelties in this framework include: 1) a hierarchal segmentation strategy including connectivity-based segmentation and determination of structure units; and 2) an automatic technique for detection and restoration of occluded structures. The experiments provide specific analysis and evaluation from three aspects: 1) correctness and completeness; 2) horizontal accuracy; and 3) elevation accuracy. The experimental results indicate that the proposed framework can provide satisfactory 3-D models of large complex bridges.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Learning Hierarchical Features for Automated Extraction of Road Markings
           From 3-D Mobile LiDAR Point Clouds
    • Authors: Yu; Y.;Li, J.;Guan, H.;Jia, F.;Wang, C.;
      Pages: 709 - 726
      Abstract: This paper presents a novel method for automated extraction of road markings directly from three dimensional (3-D) point clouds acquired by a mobile light detection and ranging (LiDAR) system. First, road surface points are segmented from a raw point cloud using a curb-based approach. Then, road markings are directly extracted from road surface points through multisegment thresholding and spatial density filtering. Finally, seven specific types of road markings are further accurately delineated through a combination of Euclidean distance clustering, voxel-based normalized cut segmentation, large-size marking classification based on trajectory and curb-lines, and small-size marking classification based on deep learning, and principal component analysis (PCA). Quantitative evaluations indicate that the proposed method achieves an average completeness, correctness, and F-measure of 0.93, 0.92, and 0.93, respectively. Comparative studies also demonstrate that the proposed method achieves better performance and accuracy than those of the two existing methods.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Anisotropic Surface Detection Over Coastal Environment Using Near-IR LiDAR
           Intensity Maps
    • Authors: Garestier; F.;Bretel, P.;Monfort, O.;Levoy, F.;Poullain, E.;
      Pages: 727 - 739
      Abstract: Near-IR LiDAR intensity maps have been investigated to detect surfaces characterized by spatially anisotropic properties. The developed estimators present an important sensitivity to the surface anisotropic properties (degree of anisotropy and relative anisotropy) and can be corrected for signal-to-noise ratio, which can be highly variable over coastal environments due to heterogeneous moisture distribution in space and time. Two methodologies are proposed to investigate the data in order to unambiguously characterize the surface anisotropic properties by separating the pure textural information from the amplitude weighted one, which provides a more relevant information in geomorphology than the pure texture. As an example, data acquired over sandy beaches are considered to illustrate the potential of surface characterization using both degree of anisotropy and associated texture amplitude. Sea surfaces are also investigated to show how does the pure texture approach allow to discriminate different sea states.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Characterizing Radiometric Attributes of Point Cloud Using a Normalized
           Reflective Factor Derived From Small Footprint LiDAR Waveform
    • Authors: Qin; Y.;Yao, W.;Vu, T.T.;Li, S.;Niu, Z.;Ban, Y.;
      Pages: 740 - 749
      Abstract: This paper presents a reflectance-like coefficient, normalized reflective factor (NRF) to characterize the radiometric attributes of point cloud generated from small footprint light detection and ranging (LiDAR) waveform data. The NRF is defined as a normalized ratio between the energy of emitted laser beam and the peak in return waveform in conjunction with the atmospheric attenuation and observation geometry. Based on the Gaussian parameters of the emitted and return waveforms, NRF is calculated with an empirical atmospheric model and user-defined standard observation geometry. To correct the radiometric measurement of point cloud in multipeak waveform, a semi-physical-based method is adopted to enhance the NRF of point cloud generated from multipeak waveform. Experiments are conducted with small footprint LiDAR waveform data acquired by RIEGL LMS-Q560. A curve-fitting-based approach is applied to decompose LiDAR waveform into three-dimensional (3-D) coordinates of point cloud, and the NRF are calculated using the Gaussian parameters of both emitted and return waveforms. The visualization of the radiometric attributes of point cloud data is carried out over the overlapping areas between different flight strips, it suggests that the NRF over overlapping area is much smooth than the normalized intensity. Quantitative comparison with Hyperion data indicates that the NRF has much higher correlation with surface reflectance than the normalized intensity data. Standard deviations of NRF and the normalized intensity of different land cover patches are analyzed to assess the homogeneity of the radiometric data. It is observed that NRF has less variability than the normalized intensity within the same land cover patches. Point cloud of two sample trees is also selected to assess the performance of the “sub-footprint” effect correction. It is observed that the proposed approach reduced the variability of radiometric attributes over tree canopies with increa- ing NRF values; which means the “sub-footprint” effect is mitigated. In summary, the proposed NRF can serve as a promising indicator to characterize radiometric attribute of LiDAR point cloud.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Estimating the Contribution of Loose Deposits to Potential Landslides Over
           Wenchuan Earthquake Zone, China
    • Authors: Zeng; T.;Ghulam, A.;Yang, W.;Grzovic, M.;Maimaitiyiming, M.;
      Pages: 750 - 762
      Abstract: Loose deposits may lead to catastrophic landslides and rockslides especially during or after heavy rainfall events. In this paper, we propose a method to estimate the volume and spatial distribution (VSD) of loose deposits using airborne and spaceborne optical remote sensing data and Interferometric Synthetic Aperture Radar (InSAR) coherence. The estimated VSD of coseismic loose deposits in Wenchuan, China, the epicenter of the Sichuan Earthquake, is then used to predict potential landslides. First, high-resolution digital elevation models (DEMs) and digital orthophoto map (DOM) are developed using stereo imagery collected by an unmanned aerial vehicle (UAV) over a core test site. Next, the VSD of loose deposits is determined using the UAV imagery, DEM, and a landslide inventory map developed by field surveys, and a power–law relationship between the area and volume is established. In order to determine the loose deposits of the entire study area, the InSAR coherence of PALSAR images collected before and right after the earthquake, Landsat image classification, and DEM combined with Analytical Hierarchy Process (AHP) model are integrated in a decision tree algorithm. Finally, we develop a quantitative method of determining the severity and likelihood of potential geohazards based on VSD of coseismic loose deposits and other variables including the Normalized Difference Vegetation Index (NDVI), slope gradient, elevation, and land-cover/land-use (LCLU). A total of 42 catchments area are identified as the hot spots with a very high or high possibility of potential landslides. The VSD of coseismic loose deposits over these catchments is about 92.34% and 77.96% of the total potential loose deposits area.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Assessment and Validation of MODIS and GEOV1 LAI With Ground-Measured Data
           and an Analysis of the Effect of Residential Area in Mixed Pixel
    • Authors: Yang; F.;Yang, J.;Wang, J.;Zhu, Y.;
      Pages: 763 - 774
      Abstract: Leaf area index (LAI) is a critical variable for simulating the carbon or nitrogen cycles and water and heat energy balance of ecosystem. MODIS and Geoland2 version 1 (GEOV1) LAI products were validated based on the ground-measured maize, winter wheat, and grass LAI data in several years. This study also investigated the residential area effect in mixed pixels on global LAI product accuracies in North Plain and Northeastern Plain in China. The MODIS and GEOV1 LAI products showed marked difference in variations of maize and winter wheat LAI at different key growth stages, and the GEOV1 LAI can present much clear differences and variations during crop growth periods. The MODIS and GEOV1 LAI products often underestimate the maize and winter wheat LAI, with the exception that GEOV1 LAI overestimate when maize LAI is large. For grass, the MODIS and GEOV1 LAI both overestimate a little. Overall, the GEOV1 LAI is often larger than the MODIS LAI. The GEOV1 LAI showed better regressions (with ${rm{R}}^{2} $ of 0.868, 0.496, and 0.216) with the ground-measured LAI than MODIS LAI (with ${rm{R}}^{2}$ of 0.258, 0.350, and 0.129) for maize, winter wheat, and grass, respectively. The residential area in mixed pixel make marked impact on MODIS and GEOV1 LAI data at different maize and winter wheat growth stages, and it maybe a main error source of the MODIS and GEOV1 LAI underestimations. The quadratic polynomial fitting relationships (most of the regressions ${rm{R}}^{2} $ exceeded 0.90) can describe well the effect of residential area percent in mixed pixel on global LAI product.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Fractional Forest Cover Changes in Northeast China From 1982 to 2011 and
           Its Relationship With Climatic Variations
    • Authors: Jia; K.;Liang, S.;Wei, X.;Li, Q.;Du, X.;Jiang, B.;Yao, Y.;Zhao, X.;Li, Y.;
      Pages: 775 - 783
      Abstract: Forest cover information is essential for natural resource management and for climate change studies. In this paper, the fractional forest cover (FFC) in Northeast China was estimated using neural networks (NNs) based on the Global Inventory Modeling and Mapping Studies (GIMMS3g) Normalized Difference Vegetation Index (NDVI) data with 8-km resolution from 1982 to 2011. Furthermore, the relationship between FFC and two key climatic parameters (temperature and precipitation) was also analyzed. The validation results indicated a satisfactory performance ( ${bf {R}}^{bf {2}} = {bf {0.81}}$ , ${bf {RMSE}} = {bf {11.7%}}$ ) of the FFC estimation method using NNs and time-series GIMMS3g NDVI data. The temporal and spatial characteristics of FFC changes were analyzed. The forest cover had a slightly decreasing trend during the study period for the entire Northeast China region. However, there were two distinct periods with opposite trends in the FFC change. The FFC had first increased from 1982 to 1998 ( ${bf {0.391%}} {bf {year}}^{{bf {- 1}}}$ ), and then decreased from 1998 to 2011 ( ${bf {- 0.667%}} {bf {year}}^{{bf {- 1}}}$ ). The correlation analysis between the FFC and the climatic variations suggested that temperature and precipitation were not the decisive factors on controlling FFC changes in most of the Northeast China regions, and active forest disturbance might be the more important factor for FFC change in Northeast China.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Assimilation of GPS-Derived Atmospheric Propagation Delay in DInSAR Data
           Processing
    • Authors: Fornaro; G.;D'Agostino, N.;Giuliani, R.;Noviello, C.;Reale, D.;Verde, S.;
      Pages: 784 - 799
      Abstract: Microwave radiation is almost insensitive in terms of power attenuation to the presence of atmosphere; the atmosphere is however an error source in repeat pass interferometry due to propagation delay variations. This effect represents a main limitation in the detection and monitoring of weak deformation patterns in differential interferometric Synthetic Aperture Radar (DInSAR), especially in emergency conditions. Due to the wavelength reduction current, X-Band sensors are even more sensitive to such error sources: procedures adopted in classical advanced DInSAR for atmospheric filtering may fail in the presence of higher revisiting rates. In this work, we show such effect on data acquired by the COSMO-SkyMed constellation. The dataset has been acquired with very high revisiting rates during the emergency phase. This feature allows clearly showing the inability of standard filtering adopted in common processing chains in handling seasonal atmospheric delay variations over temporal intervals spanning periods shorter than 1 year. We discuss a procedure for the mitigation of atmospheric propagation delay (APD) that is based on the integration of data of GPS systems which carries out measurements with large observation angles diversity practically in continuous time. The proposed algorithm allows a robust assimilation of the GPS atmospheric delay measurements in the multipass DInSAR processing and found on a linear approximation with the height of the atmospheric delay corresponding to a stratified atmosphere. Achieved results show a significant mitigation of the seasonal atmospheric variations.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Development of a Framework for Stereo Image Retrieval With Both Height and
           Planar Features
    • Authors: Peng; F.;Wang, L.;Gong, J.;Wu, H.;
      Pages: 800 - 815
      Abstract: The wide availability and increasing number of applications for high-resolution optical satellite stereo images (HrosSIs) have created a surging demand for the development of effective content-based image retrieval methods. However, this is a challenge for existing stereo image retrieval methods since they were designed for stereo images collected from close-range imaging sensors. Thus, successful retrieval of images is not assured given the mismatch between existing methods and the characteristics of HrosSIs. Moreover, none of the existing remote sensing image retrieval methods takes account of the specific characteristics of HrosSIs such as the viewing number and multiview angles. This paper proposes a generic framework to exploit the unique characteristics of HrosSIs data so as to allow efficient and accurate content-based HrosSI retrieval. HrosSIs retrieval is executed by similarity matching between the features obtained from digital surface models (DSMs) and orthoimages, both extracted from the HrosSIs. In addition, the significance of height information for HrosSI retrieval was investigated. A prototype system was designed and implemented for method validation using the ISPRS stereo benchmark test dataset. Experimental results show that the proposed techniques are efficient for HrosSI retrieval. The proposed framework is efficient and suitable for spaceborne stereo images but might also be suitable for airborne stereo images as well. Experimental results also show that height information alone is inefficient and unstable for HrosSI retrieval; however, a combination of height information and planar information is efficient and stable.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Integrating Gravity Data With Remotely Sensed Data for Structural
           Investigation of the Aynak-Logar Valley, Eastern Afghanistan, and the
           Surrounding Area
    • Authors: Azizi; M.;Saibi, H.;
      Pages: 816 - 824
      Abstract: This study integrates gravity data with interpreted lineaments from remotely sensed images and geological fault in an effort to understand the geological structure of the Aynak-Logar Valley (ALV) and its surrounding area in eastern Afghanistan. Integrated analysis of Landsat Enhanced Thematic Mapper Plus ( ${bf ETM+}$ ), Shuttle Radar Topography Mission (SRTM), and Digital Elevation Model (DEM) data was applied for lithological mapping and extraction of geological lineaments and landforms. Gravity data were used to delineate a detailed picture of the subsurface structure. Several gravity interpretation techniques such as horizontal gradient (HG), tilt derivative (TD), and analytic signal (AS) were applied to the gravity data with the objective of making geological features such as faults and contacts more visible, and also a three-dimensional (3-D) inversion model of gravity data was developed to show the density distributions in the study area. The combination of these geoscience data provides information about the subsurface structure of ALV. The interpreted faults from remote sensing are striking NE-SW. The faults and contacts from geological map and gravity data analysis are striking mainly in NNE-SSW, which is the direction of the Kabul block trending fault structure.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Extension of the Generalized Split-Window Algorithm for Land Surface
           Temperature Retrieval to Atmospheres With Heavy Dust Aerosol Loading
    • Authors: Fan; X.;Tang, B.;Wu, H.;Yan, G.;Li, Z.;Zhou, G.;Shao, K.;Bi, Y.;
      Pages: 825 - 834
      Abstract: It is worth noting that the influences of dust aerosol type and different aerosol loadings were not considered in the development of the generalized split-window (GSW) algorithm. However, numerical simulations showed that the influence of dust aerosol could lead to a maximum land surface temperature (LST) retrieval error of 5.12 K when the aerosol optical depth (AOD) in the atmosphere is 1.0 and viewing zenith angle (VZA) is 60°. This paper focuses on reducing the influence of dust aerosol on the LST retrieval error of the GSW algorithm. A linear function was developed to reduce such influence with respect to the AOD. The slope could be expressed as a function of the difference between the MODIS channel brightness temperatures ${bm{T}}_{bf{31}}$ and ${bm{T}}_{bf{32}}$ measured at the top of the atmosphere (TOA) and difference and mean of the two-channel emissivities, and the offset could be used as a constant value for each VZA. The results showed that the retrieval accuracy could be improved by approximately 4 K for ${bf{AOD}} = {bf{1.0}}$ and ${bf{VZA}} = {bf{60}}^circ$ . Sensitivity analysis in terms of the uncertainties of the input parameters showed that the maximum LST retrieval error is 1.15 K for ${bf{VZA}} = {bf{0}}^circ$ . Some of the in situ measurements observed at the Yingke site in northwest China and Arvaikheer site in south Mongolia were used to test the proposed method, respectively. The results showed that the proposed method could improve the LST retrieval accuracy by at least 1 K for the GSW algorithm in atmospheres with he- vy dust aerosol loading.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Learning Machine Identification of Ferromagnetic UXO Using Magnetometry
    • Authors: Bray; M.P.;Link, C.A.;
      Pages: 835 - 844
      Abstract: The fundamental problem in applying geophysical mapping to locate unexploded ordnance (UXO) is distinguishing true UXO from non-UXO. Enhancing the accuracy of UXO detection has multiple benefits, especially in the areas of cost savings and safety. We investigated discrimination approaches using both magnetic field data and numerically modeled data. Libraries of total field magnetic (TFM) responses were calculated using finite element modeling for three UXO types found at a Montana National Guard training site. UXO model parameters were varied over ranges of azimuth, declination, and depth resulting in approximately 600 models per UXO type. The modeled responses of finite-element model (FEM) and actual TFM field data were then used as training data in discrimination and classification approaches comparing neural networks (NN), random forests (RF), and support vector machines (SVMs). The prediction targets in the training process comprised three classes: 1) binary [UXO or noninteresting object (NIO)]; 2) multiclass (UXO round type and NIO); and 3) classes derived from multiclass self-organizing feature map (SOFM) analysis. The multiclass SOFM targets generated from site-specific field data were found to be optimal for UXO discrimination. The best performing combination of class selection types using recentered data for UXO detection rates of 100% resulted in a false alarm rate (FAR) of 28%.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Ensemble Multiple Kernel Active Learning For Classification of Multisource
           Remote Sensing Data
    • Authors: Zhang; Y.;Yang, H.L.;Prasad, S.;Pasolli, E.;Jung, J.;Crawford, M.;
      Pages: 845 - 858
      Abstract: Incorporating disparate features from multiple sources can provide valuable diverse information for remote sensing data analysis. However, multisource remote sensing data require large quantities of labeled data to train robust supervised classifiers, which are often difficult and expensive to acquire. A mixture-of-kernel approach can facilitate the construction of an effective formulation for acquiring useful samples via active learning (AL). In this paper, we propose an ensemble multiple kernel active learning (EnsembleMKL-AL) framework that incorporates different types of features extracted from multisensor remote sensing data (hyperspectral imagery and LiDAR data) for robust classification. An ensemble of probabilistic multiple kernel classifiers is embedded into a maximum disagreement-based AL system, which adaptively optimizes the kernel for each source during the AL process. At the end of each learning step, a decision fusion strategy is implemented to make a final decision based on the probabilistic outputs. The proposed framework is tested in a multisource environment, including different types of features extracted from hyperspectral and LiDAR data. The experimental results validate the efficacy of the proposed approach. In addition, we demonstrate that using ensemble classifiers and a large number of disparate but relevant features can further improve the performance of an AL-based classification approach.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Radiometric Information Content for Water Vapor and Temperature Profiling
           in Clear Skies Between 10 and 200 GHz
    • Authors: Sahoo; S.;Bosch-Lluis, X.;Reising, S.C.;Vivekanandan, J.;
      Pages: 859 - 871
      Abstract: Atmospheric profiles of water vapor and temperature can be estimated using appropriate retrieval algorithms based on radiometric measurements and atmospheric statistics. Radiometric measurements at multiple frequencies contribute information to profile retrieval, although at some frequencies the information they provide can be highly correlated with that at other frequencies due to similar sensitivities to changes in atmospheric pressure, temperature, and water vapor mixing ratio as a function of altitude. The goal for profile retrieval is to obtain as many independent measurements as possible, both to maximize the vertical resolution and to minimize the retrieval error of the profile. The goal of this study is to determine sets of frequencies in the range from 10 to 200 GHz that provide the largest amount of mutually independent information on water vapor and temperature profiles from ground and airborne instruments for clear sky measurements. Results of such a study are important and useful for frequency selection and design of microwave and millimeter-wave radiometers for humidity and temperature profiling. A branch and bound feature selection algorithm has been used to determine sets of frequencies between 10 and 200 GHz that have the greatest number of degrees of freedom (DOF) for water vapor and temperature retrieval. In general, it has been found that the frequency ranges of 20–23, 85–90, and 165–200 GHz are useful for water vapor profile retrieval, whereas the frequency ranges of 55–65 and 116–120 GHz are useful for temperature profile retrieval. Finally, an analysis has been performed to determine the impact of measurement uncertainty on the number of DOF of measurement and also on the vertical resolution. It was also found that vertical resolution is directly related to the number of DOF.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Online Monitoring of Crude Oil Biodegradation at Elevated Pressures
    • Authors: Valladares Juarez; A.G.;Kadimesetty, H.S.;Achatz, D.E.;Schedler, M.;Muller, R.;
      Pages: 872 - 878
      Abstract: In order to study the biodegradation of crude oil spilled in the deep sea, incubations of deep-sea-bed sediments and crude oil were carried out in a high-pressure reactor, but monitoring the biodegradation of oil at high pressure is limited by sampling because the volatile crude oil components are partly lost during depressurization. Moreover, the seawater-oil-sediments multiphase system cannot be sampled representatively. The aerobic oil biodegradation can also be monitored indirectly by measuring the oxygen consumed and the carbon dioxide produced. In this paper, the O2 and CO2 concentrations were monitored in a reactor with transparent windows using chemical-optical sensors. To compare the effect of pressure on the biodegradation of oil, two pressure regimes were compared: atmospheric pressure (1 bar) and 150 bar, corresponding to 1500 m depth of the Deepwater Horizon’s well at the Gulf of Mexico. Only in the experiments where deep-sea sediments were added, the oxygen concentration decreased while the carbon dioxide and the bacterial concentration increased. In experiments where no sediment was added, the values for the oxygen and carbon dioxide remained constant. This proved that deep-sea sediments contained microorganisms, which could degrade crude oil at both 1 and 150 bar. To our knowledge, this is the first time where O2 and CO2 were monitored online during crude oil biodegradation at high pressure in the laboratory.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Fast Time-Domain Imaging in Elliptical Polar Coordinate for General
           Bistatic VHF/UHF Ultra-Wideband SAR With Arbitrary Motion
    • Authors: Xie; H.;An, D.;Huang, X.;Zhou, Z.;
      Pages: 879 - 895
      Abstract: In this paper, a fast time-domain imaging algorithm called bistatic fast factorized back projection algorithm (BFFBPA) is proposed for the general bistatic VHF/UHF ultra-wideband synthetic aperture radar. It cannot only accurately dispose the large spatial variant range cell migrations, serious range-azimuth coupling and complicated motion error, but also achieve the imaging efficiency similar to frequency-domain algorithms. It represents subimages in elliptical polar coordinate to reduce the computational load. The imaging geometry with arbitrary motion in this coordinate system is provided, and the bistatic back projection algorithm (BPA) is derived to provide a basis for the proposed BFFBPA. Considering motion errors, the more accurate sampling requirements for elliptical subimages is deduced to offer the near-optimum tradeoff between the imaging quality and efficiency, and the constraint of motion errors for acceptable sampling requirements is discussed. Based on this sampling requirement, the advantage of using elliptical subimage grids for this BFFBPA is analyzed. A phase error correction is performed to reduce the impact of phase errors caused by interpolations in the BFFBPA. The speed-up factor of this BFFBPA with respect to the bistatic BPA is derived. Simulation results and evaluations are given to prove the correctness of the theory analysis and validity of the proposed method.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Comparison of Algorithms for Wind Parameters Extraction From Shipborne
           X-Band Marine Radar Images
    • Authors: Liu; Y.;Huang, W.;Gill, E.W.;Peters, D.K.;Vicen-Bueno, R.;
      Pages: 896 - 906
      Abstract: In this paper, curve-fitting and intensity-level-selection (ILS)-based algorithms for wind parameter extraction from shipborne X-band nautical radar images are investigated. First, to exclude the rain cases and low-backscatter images, a data quality control process is designed for both algorithms. An additional process is then introduced for the ILS-based method to improve the accuracy of wind measurements, including the recognition of blockages and islands in the temporally integrated radar images. For the low sea states, a dual-curve-fitting is proposed. These wind algorithms are tested using radar images and shipborne anemometer data collected on the east coast of Canada. It is shown that the dual-curve-fitting algorithm produces improvements in the mean differences between the radar and the anemometer results for wind direction and speed of about 5.7° and 0.3 m/s, respectively, under sea states with significant wave height lower than 2.30 m. Also, a harmonic function that is least-squares fitted to the selected range distances vector as a function of antenna look direction is applied. Compared with the original ILS-based algorithm, the modified procedure reduces the standard deviation for wind direction and speed by about 4° and 0.2 m/s, respectively. Finally, the performance of these two modified methods are compared.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • MODIS-Based Radiometric Color Extraction and Classification of Inland
           Water With the Forel-Ule Scale: A Case Study of Lake Taihu
    • Authors: Wang; S.;Li, J.;Shen, Q.;Zhang, B.;Zhang, F.;Lu, Z.;
      Pages: 907 - 918
      Abstract: Serious difficulties are present in the application of remote sensing techniques for optically complex waters, as retrieval of water quality parameters is generally based on detailed local knowledge of optical properties of water bodies for specific areas and at specific times. Water color is measured in traditional water quality observations and characterized by the Forel-Ule scale, as it is intimately related to water compositions. In this paper, a Moderate Resolution Imaging Spectroradiometer (MODIS) based water color extraction and classification approach is developed and applied to Lake Taihu. By using MODIS data together with field data, we attempted to 1) retrieve the dominant wavelength of water color and classify water color into FU-classes; 2) analyze the relationship between water color dominant wavelength and the abundance of optically active component (OACs) in water; and 3) discover seasonal variations of water color based on Lake Taihu. Our results show that the dominant wavelength exhibits some relationship with the three types of OAC concentrations under certain conditions, particularly TSM and Chl-a; inorganic suspended matter (ISM) can be retrieved by using MODIS derived dominant wavelength in appropriate water body. Moreover, differences in water quality for different seasons can be detected by dominant wavelength and FU-class with some prior knowledge of the studied water. Therefore, dominant wavelength may be used as a comprehensive and promising indicator of water quality situation even though much work has to be done in the future to optimize the analyses and verify it on diverse sites.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Time-Varying Elevation Change at the Centralia Coal Mine in Centralia,
           Washington (USA), Constrained with InSAR, ASTER, and Optical Imagery
    • Authors: Prush; V.B.;Lohman, R.B.;
      Pages: 919 - 925
      Abstract: Rapid landscape changes as a result of anthropogenic development can result in increased hazards for nearby communities, including exposure of soils to runoff, pollution, and slope collapse. Elevation changes also complicate interpretations of remote sensing datasets, such as interferometric synthetic aperture radar (InSAR), which rely on accurate digital elevation models (DEMs). In this study, we assimilate satellite-based radar and optical imagery spanning several decades to determine a time series of elevation change at the Centralia Coal Mine in Centralia, Washington. We assess the errors associated with each observation type and explore methods for reducing potential bias. By combining these datasets, we are able to characterize elevation changes resulting from coal mining operations for most of the productive lifespan of the mine. This approach can be applied to any region of rapid elevation change, provided sufficient remote sensing data are available.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Adding Geospatial Data Provenance into SDI—A Service-Oriented
           Approach
    • Authors: He; L.;Yue, P.;Di, L.;Zhang, M.;Hu, L.;
      Pages: 926 - 936
      Abstract: Geospatial data provenance records the derivation history of a geospatial data product. It is important in evaluating the quality of data products. In a Geospatial Web Service environment where data are often disseminated and processed widely and frequently in an unpredictable way, it is even more important in identifying original data sources, tracing workflows, updating or reproducing scientific results, and evaluating reliability and quality of geospatial data products. Geospatial data provenance has become a fundamental issue in establishing the spatial data infrastructure (SDI). This paper investigates how to support provenance awareness in SDI. It addresses key issues including provenance modeling, capturing, and sharing in a SDI enabled by interoperable geospatial services. A reference architecture for provenance tracking is proposed, which can accommodate geospatial feature provenance at different levels of granularity. Open standards from ISO, World Wide Web Consortium (W3C), and OGC are leveraged to facilitate the interoperability. At the feature type level, this paper proposes extensions of W3C PROV-XML for ISO 19115 lineage and “Parent Level” provenance registration in the geospatial catalog service. At the feature instance level, light-weight lineage information entities for feature provenance are proposed and managed by Web Feature Services. Experiments demonstrate the applicability of the approach for creating provenance awareness in an interoperable geospatial service-oriented environment.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • Earth Science Satellite Applications: Current and Future Prospects
    • Pages: 937 - 937
      Abstract: Describes the above-named upcoming special issue or section. May include topics to be covered or calls for papers.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • IEEE Geoscience and Remote Sensing Magazine
    • Pages: 938 - 938
      Abstract: Describes the above-named upcoming special issue or section. May include topics to be covered or calls for papers.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
  • IEEE Member Digital Library
    • Pages: 940 - 940
      Abstract: Advertisement: The IEEE Member Digital Library brings you access to IEEE journals, magazines and conference papers published today or in the last five years. Full-text access to the most essential information in technology today with one convenient subscription. Subscribe: www.ieee.org/ieeemdl.
      PubDate: Feb. 2015
      Issue No: Vol. 8, No. 2 (2015)
       
 
 
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