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    • Plusieurs versions

    Introduction to remote sensing

    Campbell, James B., 1944-
    • Article
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    Simulation of lidar waveforms with a time-dependent radiosity algorithm

    Huang, Huaguo, Wynne, Randolph H
    Canadian Journal of Remote Sensing, 07 June 2013, Vol.39, pp.S126-S138 [Revue évaluée par les pairs]
    Taylor & Francis (Taylor & Francis Group)
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    Titre: Simulation of lidar waveforms with a time-dependent radiosity algorithm
    Auteur: Huang, Huaguo; Wynne, Randolph H
    Sujet: Geography
    Description: Our objective was to assess the effect of multiple scattering on lidar radiative transfer. We have developed a time-dependent radiosity-based model (RBL) to simulate the propagation of lidar pulses through forest canopies. This 3-D model enables simulation of lidar waveforms with varied topography and clumping vegetation. The incidence angle can also be specified. This new model has the potential to provide better approximations of return waveforms. The prototype is being tested using data from the Scanning Lidar Imager of Canopies by Echo Recovery (SLICER). Waveforms simulated by RBL resemble SLICER waveforms (R 2 > 0.90) over a jack pine canopy and a black spruce canopy. There is also good agreement (R 2 > 0.95) when the model results are compared with a time-dependent radiative transfer model. Results to date indicate that multiply scattered photons do increase the intensity of the reflected...
    Fait partie de: Canadian Journal of Remote Sensing, 07 June 2013, Vol.39, pp.S126-S138
    Identifiant: 0703-8992 (ISSN); 1712-7971 (E-ISSN); 10.5589/m13-035 (DOI)

    • Plusieurs versions

    Estimating leaf area index in intensively managed pine plantations using airborne laser scanner data

    Peduzzi, Alicia, Wynne, Randolph H, Fox, Thomas R, Nelson, Ross F, Thomas, Valerie A
    Forest Ecology and Management, 15 April 2012, Vol.270, pp.54-65 [Revue évaluée par les pairs]
    ScienceDirect Journals (Elsevier)
    • Plusieurs versions

    A mid‐century ecological forecast with partitioned uncertainty predicts increases in loblolly pine forest productivity

    Thomas, R. Quinn, Jersild, Annika L., Brooks, Evan B., Thomas, Valerie A., Wynne, Randolph H.
    Ecological Applications, September 2018, Vol.28(6), pp.1503-1519 [Revue évaluée par les pairs]

    • Plusieurs versions

    Improving within-genus tree species discrimination using the discrete wavelet transform applied to airborne hyperspectral data

    Banskota, Asim, Wynne, Randolph H, Kayastha, Nilam
    International Journal of Remote Sensing, 10 July 2011, Vol.32(13), pp.3551-3563 [Revue évaluée par les pairs]

    • Plusieurs versions

    Synergistic use of very high-frequency radar and discrete-return lidar for estimating biomass in temperate hardwood and mixed forests

    Banskota, Asim, Wynne, Randolph, Johnson, Patrick, Emessiene, Bomono
    Annals of Forest Science, 2011, Vol.68(2), pp.347-356 [Revue évaluée par les pairs]

    • Plusieurs versions

    Rebuilding the Brazilian rainforest: Agroforestry strategies for secondary forest succession

    Blinn, Christine E, Browder, John O, Pedlowski, Marcos A, Wynne, Randolph H
    Applied Geography, September 2013, Vol.43, pp.171-181 [Revue évaluée par les pairs]

    • Article
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    Virginia’s Efforts to Expand Learning Geospatial Technologies Across the Educational Spectrum

    Parece, Tammy E, Mcgee, John, Campbell, James B, Wynne, Randolph H
    Photogrammetric Engineering & Remote Sensing, March 2015, Vol.81(3), pp.177-185 [Revue évaluée par les pairs]
    ScienceDirect Journals (Elsevier)
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    Titre: Virginia’s Efforts to Expand Learning Geospatial Technologies Across the Educational Spectrum
    Auteur: Parece, Tammy E; Mcgee, John; Campbell, James B; Wynne, Randolph H
    Sujet: Engineering
    Fait partie de: Photogrammetric Engineering & Remote Sensing, March 2015, Vol.81(3), pp.177-185
    Identifiant: 0099-1112 (ISSN); 2374-8079 (E-ISSN); 10.1016/S0099-1112(15)30335-9 (DOI)

    • Plusieurs versions

    Mapping the height and spatial cover of features beneath the forest canopy at small-scales using airborne scanning discrete return Lidar

    Sumnall, Matthew, Fox, Thomas R, Wynne, Randolph H, Thomas, Valerie A
    ISPRS Journal of Photogrammetry and Remote Sensing, November 2017, Vol.133, pp.186-200 [Revue évaluée par les pairs]

    • Article
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    Approximating Prediction Uncertainty for Random Forest Regression Models

    Coulston, John W, Blinn, Christine E, Thomas, Valerie A, Wynne, Randolph H
    Photogrammetric Engineering & Remote Sensing, March 2016, Vol.82(3), pp.189-197 [Revue évaluée par les pairs]
    ScienceDirect Journals (Elsevier)
    Disponible
    Plus…
    Titre: Approximating Prediction Uncertainty for Random Forest Regression Models
    Auteur: Coulston, John W; Blinn, Christine E; Thomas, Valerie A; Wynne, Randolph H
    Sujet: Engineering
    Description: Machine learning approaches such as random forest have increased for the spatial modeling and mapping of continuous variables. Random forest is a non-parametric ensemble approach, and unlike traditional regression approaches there is no direct quantification of prediction error. Understanding prediction uncertainty is important when using model-based continuous maps as inputs to other modeling applications such as fire modeling. Here we use a Monte Carlo approach to quantify prediction uncertainty for random forest regression models. We test the approach by simulating maps of dependent and independent variables with known characteristics and comparing actual errors with prediction errors. Our approach produced conservative prediction intervals across most of the range of predicted values. However, because the Monte Carlo approach was data driven, prediction intervals were either too wide or too narrow in sparse parts of the prediction distribution. Overall, our approach provides...
    Fait partie de: Photogrammetric Engineering & Remote Sensing, March 2016, Vol.82(3), pp.189-197
    Identifiant: 0099-1112 (ISSN); 2374-8079 (E-ISSN); 10.14358/PERS.82.3.189 (DOI)