Tree species identification in mixed baltic forest using LiDAR and multispectral data

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

Volumn 5, Issue 2, 2012, Pages 594-603

  Dinuls, Romans ^a   Erins, Gatis ^b   Lorencs, Aivars ^a   Mednieks, Ints ^a   Sinica Sinavskis, Juris ^a  

^a INSTITUTE OF ELECTRONICS AND COMPUTER SCIENCE   (Latvia)

^b Institute for Environmental Solutions   (Latvia)

Author keywords

Forest inventory; multi spectral imaging; tree species identification

Indexed keywords

BAYES CLASSIFIER; CLASSIFICATION ALGORITHM; CLASSIFICATION APPROACH; CLASSIFICATION RESULTS; CLASSIFIER DESIGN; CLUSTER PROPERTY; CORE PROBLEMS; DATA ACQUISITION EQUIPMENT; DECIDUOUS SPECIES; FIELD WORKS; FOREST AREA; FOREST INVENTORY; INDIVIDUAL TREE; INITIAL DESIGN; MAJORITY VOTE; MAXIMUM ERROR; MEAN CLASSIFICATION; MIXED FORESTS; MULTI-CLASS CLASSIFIER; MULTI-SPECTRAL DATA; MULTI-SPECTRAL IMAGERY; MULTIPLE CLUSTERINGS; MULTISPECTRAL IMAGING; PRE-PROCESSING SOFTWARE; PROCESSING APPROACH; SEMI-AUTOMATED; TREE SPECIES; TREE TOPS;

CLASSIFICATION (OF INFORMATION); DESIGN; OPTICAL RADAR;

FORESTRY;

ALGORITHM; BAYESIAN ANALYSIS; DATA ACQUISITION; ERROR ANALYSIS; FOREST INVENTORY; IDENTIFICATION METHOD; IMAGE CLASSIFICATION; LIDAR; MIXED FOREST; MULTISPECTRAL IMAGE; PARAMETERIZATION; SATELLITE DATA; SOFTWARE;

BIODIVERSITY; CLASSIFICATION; FORESTS; INVENTORY CONTROL; RADAR;

BALTIC REGION;

CONIFEROPHYTA;

EID: 84861740931     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2012.2196978     Document Type: Article

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