Mapping a priori defined plant associations using remotely sensed vegetation characteristics

Remote Sensing of Environment

Volumn 140, Issue , 2014, Pages 639-651

  Roelofsen, Hans D ^a   Kooistra, Lammert ^b   Van Bodegom, Peter M ^c   Verrelst, Jochem ^d   Krol, Johan ^e   Witte, Jan Philip M ^a,c  

^a KWR WATERCYCLE RESEARCH INSTITUTE   (Netherlands)

^b WAGENINGEN UNIVERSITY   (Netherlands)

^c VU UNIVERSITY AMSTERDAM   (Netherlands)

^d UNIVERSITY OF VALENCIA   (Spain)

^e Nature Centre Ameland   (Netherlands)

Author keywords

Coastal ecosystem; Gaussian Mixture Density Modelling; Gaussian Process Regression; Indicator values; Optical airborne remote sensing; Phytosociology; Probability Density Function

Indexed keywords

AIRBORNE REMOTE SENSING; COASTAL ECOSYSTEMS; GAUSSIAN MIXTURE DENSITIES; GAUSSIAN PROCESS REGRESSION; INDICATOR VALUES; PHYTOSOCIOLOGY;

ECOSYSTEMS; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); MOISTURE; NUTRIENTS; PIXELS; PROBABILITY; PROBABILITY DENSITY FUNCTION; REFLECTION; SITE SELECTION; VEGETATION;

REMOTE SENSING;

BIOINDICATOR; COMMUNITY STRUCTURE; DATABASE; GAUSSIAN METHOD; GROUND PENETRATING RADAR; MAPPING METHOD; MOISTURE; NUTRIENT CYCLING; PIXEL; PREDICTION; REMOTE SENSING; SALINITY; SPATIAL DISTRIBUTION; SPECTRAL REFLECTANCE; VEGETATION STRUCTURE;

NETHERLANDS;

EID: 84886404125     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2013.09.030     Document Type: Article

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