Machine learning approaches for forest classification and change analysis using multi-temporal Landsat TM images over Huntington Wildlife Forest

GIScience and Remote Sensing

Volumn 50, Issue 4, 2013, Pages 361-384

  Li, Manqi ^a   Im, Jungho ^a   Beier, Colin ^b  

^a Ulsan National Institute of Science and Technology   (South Korea)

^b STATE UNIVERSITY OF NEW YORK   (United States)

Author keywords

change detection; decision trees; forest type classification; random forest; remote sensing; support vector machines; topographic correction

Indexed keywords

ALGORITHM; ARTIFICIAL INTELLIGENCE; CLASSIFICATION; EMPIRICAL ANALYSIS; FOREST ECOSYSTEM; LANDSAT THEMATIC MAPPER; REMOTE SENSING; SATELLITE DATA; TERRAIN; TOPOGRAPHY;

HUNTINGTON WILDLIFE FOREST; NEW YORK [UNITED STATES]; UNITED STATES;

EID: 84883537640     PISSN: 15481603     EISSN: None     Source Type: Journal    
DOI: 10.1080/15481603.2013.819161     Document Type: Article

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