Strategies for minimizing sample size for use in airborne LiDAR-based forest inventory

Forest Ecology and Management

Volumn 292, Issue , 2013, Pages 75-85

  Junttila, Virpi ^a   Finley, Andrew O ^b   Bradford, John B ^c   Kauranne, Tuomo ^a  

^a LAPPEENRANTA UNIVERSITY OF TECHNOLOGY   (Finland)

^b MICHIGAN STATE UNIVERSITY   (United States)

^c U S GEOLOGICAL SURVEY   (United States)

Author keywords

Bayesian; Biomass; Geostatistical; LiDAR; Optimal design

Indexed keywords

ABOVE GROUND BIOMASS; BAYESIAN; CALIBRATION PLOT; FEATURE SPACE; FIELD PLOT; FOREST INVENTORY; FOREST PARAMETERS; GEOSTATISTICAL; LIGHT DETECTION AND RANGING; OPERATIONAL SCALE; OPTIMAL DESIGN; POINT CLOUD; PREDICTIVE VARIABLES; RANDOM METHODS; REGRESSION MODEL; REPRESENTATIVITY; SAMPLE SIZES; SENSING MODALITIES;

BIOMASS; FORESTRY; REGRESSION ANALYSIS; REMOTE SENSING;

OPTICAL RADAR;

ABOVEGROUND BIOMASS; ACCURACY ASSESSMENT; BAYESIAN ANALYSIS; BIOMASS; CALIBRATION; ECOLOGICAL MODELING; FOREST INVENTORY; GEOSTATISTICS; LIDAR; REMOTE SENSING; SPATIAL VARIATION;

AUTOMATIC CONTROL; BIOMASS; OPTICAL ACTIVITY; OPTIMIZATION; PERFORMANCE; RADAR; REGRESSION ANALYSIS; REMOTE SENSING; STATISTICAL ANALYSIS;

EID: 84872591926     PISSN: 03781127     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.foreco.2012.12.019     Document Type: Article

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