Hierarchical Bayesian models for small area estimation of forest variables using LiDAR

Remote Sensing of Environment

Volumn 204, Issue , 2018, Pages 287-295

  Ver Planck, Neil R ^a   Finley, Andrew O ^a   Kershaw, John A ^b   Weiskittel, Aaron R ^c   Kress, Megan C ^a  

^a MICHIGAN STATE UNIVERSITY   (United States)

^b UNIVERSITY OF NEW BRUNSWICK   (Canada)

^c UNIVERSITY OF MAINE   (United States)

Author keywords

Aboveground forest biomass; Area level; Hierarchical Bayes; LiDAR; small area estimation

Indexed keywords

BARIUM COMPOUNDS; BAYESIAN NETWORKS; OPTICAL RADAR; RANDOM PROCESSES; REMOTE SENSING;

ABOVEGROUND FOREST BIOMASS; AREA-LEVEL; COEFFICIENTS OF VARIATIONS; CONDITIONAL AUTOREGRESSIVE; FOREST BIOMASS; FOREST INVENTORY; HIERARCHICAL BAYE; LIGHT DETECTION AND RANGING; RANDOM EFFECTS; SMALL AREA ESTIMATION;

FORESTRY;

ABOVEGROUND BIOMASS; ACCURACY ASSESSMENT; BAYESIAN ANALYSIS; ECOLOGICAL MODELING; ESTIMATION METHOD; FOREST; FOREST INVENTORY; FORESTRY; HIERARCHICAL SYSTEM; LIDAR; PRECISION; REMOTE SENSING; SPECIES-AREA RELATIONSHIP;

EID: 85033471934     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2017.10.024     Document Type: Article

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