Multivariate spatial regression models for predicting individual tree structure variables using LiDAR data

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 6, Issue 1, 2013, Pages 6-14

  Babcock, Chad ^a   Matney, Jason ^a   Finley, Andrew O ^a   Weiskittel, Aaron ^b   Cook, Bruce D ^c  

^a MICHIGAN STATE UNIVERSITY   (United States)

^b UNIVERSITY OF MAINE   (United States)

^c NASA GODDARD SPACE FLIGHT CENTER   (United States)

Author keywords

Bayesian; Forestry; Gaussian process; LiDAR; MCMC; Spatial random effects

Indexed keywords

FORECASTING; MATHEMATICAL MODELS; OPTICAL RADAR; RANDOM PROCESSES; REGRESSION ANALYSIS;

BAYESIAN; DIAMETER-AT-BREAST HEIGHTS; GAUSSIAN PROCESSES; LIGHT DETECTION AND RANGING; MCMC; PENOBSCOT EXPERIMENTAL FORESTS; RANDOM EFFECTS; SPATIAL REGRESSION MODEL;

FORESTRY;

BAYESIAN ANALYSIS; ERROR ANALYSIS; FORESTRY; GAUSSIAN METHOD; LIDAR; MULTIVARIATE ANALYSIS; PREDICTION; REGRESSION ANALYSIS; SPATIAL ANALYSIS; SPATIAL RESOLUTION;

FORECASTS; FORESTRY; MATHEMATICAL MODELS; RADAR; RANDOM PROCESSES; REGRESSION ANALYSIS;

MAINE; PENOBSCOT EXPERIMENTAL FOREST; UNITED STATES;

EID: 84880266886     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2012.2215582     Document Type: Article

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