A support vector regression approach to estimate forest biophysical parameters at the object level using airborne lidar transects and quick bird data

Photogrammetric Engineering and Remote Sensing

Volumn 77, Issue 7, 2011, Pages 733-741

  Chen, Gang ^a   Hay, Geoffrey J ^a  

^a UNIVERSITY OF CALGARY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

BIRDS; FORESTRY; LEARNING SYSTEMS; ORBITS; REGRESSION ANALYSIS; SATELLITE IMAGERY; TRACKING (POSITION);

ABOVE GROUND BIOMASS; BIOPHYSICAL PARAMETERS; COMPLEX RELATIONSHIPS; MACHINE LEARNING TECHNIQUES; MULTIPLE REGRESSIONS; OPTICAL SATELLITE IMAGERY; SUPPORT VECTOR REGRESSION (SVR); VERTICAL STRUCTURES;

OPTICAL RADAR;

AIRBORNE SENSING; BIOPHYSICS; CANOPY ARCHITECTURE; CONIFEROUS TREE; DATA SET; DECIDUOUS TREE; FOREST CANOPY; HEIGHT DETERMINATION; IMAGE ANALYSIS; LIDAR; MULTIPLE REGRESSION; QUICKBIRD; SATELLITE DATA; SATELLITE IMAGERY; VECTOR AUTOREGRESSION;

FORESTRY; IMAGE ANALYSIS; OPTICAL ACTIVITY; PARAMETERS; RADAR; REGRESSION ANALYSIS; SATELLITES;

AVES; CONIFEROPHYTA;

EID: 79960462309     PISSN: 00991112     EISSN: None     Source Type: Journal    
DOI: 10.14358/PERS.77.7.733     Document Type: Article

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