Support vector regression for the estimation of forest stand parameters using airborne laser scanning

IEEE Geoscience and Remote Sensing Letters

Volumn 8, Issue 3, 2011, Pages 580-584

  Monnet, Jean Matthieu ^a   Chanussot, Jocelyn ^b   Berger, Fŕd́ric ^a  

^a Cemagref   (France)

^b GIPSA LAB   (France)

Author keywords

Airborne laser scanning; dimension reduction (DR); entropy; forestry; prediction methods; remote sensing; support vector regression (SVR)

Indexed keywords

AIRBORNE LASER SCANNING; ARTIFICIAL NEURAL NETWORK MODELS; BASAL AREA; DATA SETS; DIMENSION REDUCTION; DIMENSION REDUCTION (DR); DOMINANT HEIGHT; FIELD CALIBRATION; FIELD DATA COLLECTION; FOREST PARAMETERS; FOREST STAND; HIGH-DIMENSIONAL; HORIZONTAL DISTRIBUTION; INPUT VARIABLES; LASER DATA; MEAN DIAMETER; MOUNTAINOUS AREA; MULTIPLE REGRESSIONS; OPTIMAL COMBINATION; ORDINARY LEAST SQUARES; PARAMETER PREDICTION; POINT CLOUD; PREDICTION METHODS; PREDICTION MODEL; PRINCIPAL COMPONENTS; REGRESSION MODEL; STEM DENSITY; SUPPORT VECTOR REGRESSION (SVR); SUPPORT VECTOR REGRESSIONS; TESTING DATA; VERTICAL DISTRIBUTIONS;

ENTROPY; FORECASTING; FORESTRY; LASER APPLICATIONS; MATHEMATICAL MODELS; NEURAL NETWORKS; PARAMETER ESTIMATION; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS; REMOTE SENSING; SCANNING; TIMBER; VECTORS;

DATA REDUCTION;

DATA PROCESSING; ENTROPY; FORECASTS; FORESTRY; FORESTS; LASERS; MATHEMATICAL MODELS; NEURAL NETWORKS; PARAMETERS; REGRESSION ANALYSIS; REMOTE SENSING;

EID: 79955522648     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2010.2094179     Document Type: Article

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