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dc.contributor Bykerk-Kauffman, Ann en
dc.contributor.advisor Shapiro, Russell S. en
dc.contributor.author Stambaugh, Jordan E. en
dc.contributor.other Mayor, Shane D. en
dc.contributor.other Miller, Ryan G. en
dc.date.accessioned 2017-03-20T20:12:40Z en
dc.date.available 2017-03-20T20:12:40Z en
dc.date.issued 2017-03-20 en
dc.identifier.uri http://hdl.handle.net/10211.3/188606 en
dc.description.abstract The rapid decline of rainforests due to human activities was the motivation for this study, which hopes to bring attention to a patch of rainforest in Papua New Guinea (home to some of the most pristine forests in the world). I utilized remote sensing to supplement previous field surveys in an attempt to classify parts of the caldera surrounding Lake Hargy (New Britain, Papua New Guinea) that were not surveyed. Five classification techniques were used, and spectral angle mapper (SAM) was found to be most effective due to the small size of the training samples, followed by ISODATA and decision trees (DTs). The other two classification techniques worked either improperly or not at all. Classification varied by image, which I attributed to seasonal changes in vegetation morphology in the area. In future studies, I hope to use higher resolution imagery to do a more detailed and consistent classification, as well as more (and larger) sample sites to increase accuracy. This will allow me to use other techniques within ArcMap and ENVI that were discussed but never used. en
dc.description.sponsorship CSU, Chico en
dc.language.iso en_US en
dc.subject Rainforests en
dc.subject Remote sensing en
dc.subject Tree classification en
dc.subject Forest composition en
dc.title Remote sensing of tropical forest tree species composition in Papua New Guinea using multi-band spectral analysis en
dc.college Natural Sciences en
dc.program Environmental Science en
dc.degree MS en


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