A Bayesian hierarchical model for estimating spatial and temporal variation in vegetation phenology from Landsat time series

Remote Sensing of Environment

Volumn 194, Issue , 2017, Pages 155-160

  Senf, Cornelius ^a   Pflugmacher, Dirk ^a   Heurich, Marco ^b   Krueger, Tobias ^a  

^a HUMBOLDT UNIVERSITY   (Germany)

^b BAVARIAN FOREST NATIONAL PARK   (Germany)

Author keywords

Bavarian Forest; Climate change; Hierarchical model; Vegetation phenology

Indexed keywords

BAYESIAN NETWORKS; BIOLOGY; CLIMATE CHANGE; CLIMATE MODELS; FORESTRY; HIERARCHICAL SYSTEMS; RADIOMETERS; RANDOM PROCESSES; SATELLITE IMAGERY; TIME SERIES; VEGETATION;

BAVARIAN FOREST NATIONAL PARK; BAVARIANS; BAYESIAN HIERARCHICAL MODEL; HIERARCHICAL MODEL; OBSERVATION FREQUENCIES; SATELLITE OBSERVATIONS; SPATIAL AND TEMPORAL VARIATION; VEGETATION PHENOLOGY;

UNCERTAINTY ANALYSIS;

ANNUAL VARIATION; CLIMATE CHANGE; FOREST ECOSYSTEM; GLOBAL CHANGE; GROUND-BASED MEASUREMENT; LANDSAT; MODIS; NUMERICAL METHOD; NUMERICAL MODEL; PHENOLOGY; SATELLITE SENSOR; SPATIAL RESOLUTION; SPATIAL VARIATION; TEMPORAL VARIATION; TIME SERIES; VEGETATION COVER;

BAVARIA; BAVARIAN FOREST NATIONAL PARK; GERMANY;

EID: 85016397067     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2017.03.020     Document Type: Article

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