Predicting fine-scale tree species abundance patterns using biotic variables derived from LiDAR and high spatial resolution imagery

Remote Sensing of Environment

Volumn 150, Issue , 2014, Pages 120-131

  Van Ewijk, Karin Y ^a   Randin, Christophe F ^b   Treitz, Paul M ^a   Scott, Neal A ^a  

^a QUEEN S UNIVERSITY   (Canada)

^b UNIVERSITY OF BASEL   (Switzerland)

Author keywords

Biotic versus abiotic environmental factors; Boosted regression trees; High spatial resolution multispectral imagery; LiDAR; Tree species abundance modeling

Indexed keywords

OPTICAL RADAR; POPULATION DISTRIBUTION; REGRESSION ANALYSIS; REMOTE SENSING; THREE DIMENSIONAL; VEGETATION;

BOOSTED REGRESSION TREES; ENSEMBLE CLASSIFICATION; ENVIRONMENTAL FACTORS; HIGH SPATIAL RESOLUTION IMAGERY; LIGHT DETECTION AND RANGING; MULTI-SPECTRAL IMAGERY; MULTISPECTRAL REMOTE SENSING; TREE SPECIES;

FORESTRY;

ABUNDANCE; LANDSCAPE; LIDAR; REGRESSION ANALYSIS; SATELLITE DATA; SATELLITE IMAGERY; SPATIAL RESOLUTION; TREE; VEGETATION CLASSIFICATION; VEGETATION COVER;

FORESTRY; MODELS; PLANTS; RADAR; REGRESSION ANALYSIS; REMOTE SENSING; SPECIES IDENTIFICATION; THREE DIMENSIONAL DESIGN; TREES;

CANADA; GREAT LAKES [NORTH AMERICA]; KANSAS; LAWRENCE; ONTARIO [CANADA]; SAINT LAWRENCE ISLANDS NATIONAL PARK; UNITED STATES;

EID: 84900840804     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2014.04.026     Document Type: Article

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