Open Journal of Forestry

Open Journal of Forestry

ISSN Print: 2163-0429
ISSN Online: 2163-0437
www.scirp.org/journal/ojf
E-mail: ojf@scirp.org
"A Comparison of Selected Parametric and Non-Parametric Imputation Methods for Estimating Forest Biomass and Basal Area"
written by Donald Gagliasso, Susan Hummel, Hailemariam Temesgen,
published by Open Journal of Forestry, Vol.4 No.1, 2014
has been cited by the following article(s):
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[15] Estimating Forest Inventory Attributes Using Airborne LiDAR in Southwestern Oregon
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[16] Comparison of satellite-based estimates of aboveground biomass in coppice oak forests using parametric, semiparametric, and nonparametric modeling methods
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[17] A comparison of imputation approaches for estimating forest biomass using landsat time-series and inventory data
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[18] Development and assessment of regeneration imputation models for National Forests of Oregon and Washington
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[19] Modeling aboveground carbon stock of Zagros forests using field data and Landsat 8 imagery
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[20] Modelagem da progressão da DBO obtida na incubação de esgoto doméstico sob diferentes temperaturas
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[22] Modeling of BOD progression obtained in sewage incubated under different temperatures
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[23] Dinámica de la biomasa aérea derivada de un programa de reforestación en San Luis Potosí
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[24] Multi-source forest inventory data for forest production and utilization analyses at different levels
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[25] Transdisciplinary Foundations of Geospatial Data Science
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[26] Mapping growing stock volume and forest live biomass: a case study of the Polissya region of Ukraine
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[28] Evaluation of Radiometric and Atmospheric Correction Algorithms for Aboveground Forest Biomass Estimation Using Landsat 5 TM Data
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[29] Optimizing nearest neighbour configurations for airborne laser scanning-assisted estimation of forest volume and biomass
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[30] Comparing Modeling Methods for Predicting Forest Attributes Using LiDAR Metrics and Ground Measurements
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[31] Modelling aboveground forest biomass using airborne laser scanner data in the miombo woodlands of Tanzania
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[32] Evaluation of the spatial linear model, random forest and gradient nearest-neighbour methods for imputing potential productivity and biomass of the Pacific Northwest forests
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[33] Stratified aboveground forest biomass estimation by remote sensing data
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[34] Anomalies in Landsat Imagery and Imputation
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[35] A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables
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[36] Evaluation of the spatial linear model, random forest and gradient nearest-neighbour methods for imputing potential productivity and biomass of the Pacific Northwest …
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[37] Evaluation of the spatial linear model, random forest and gradient nearest-neighbour methods for imputing potential productivity and biomass of the Pacific Northwest …
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[38] Characteriation of Mediterranean Aleppo pine forest using low-density ALS data
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