Event Title
Land Use/Land Cover Change in Manaus, Brazil
Presentation Type
Oral and/or Visual Presentation
Presenter Major(s)
Geography and Planning
Mentor Information
Wanxiao Sun
Department
Geography and Planning
Location
Kirkhof Center 2270
Start Date
11-4-2012 4:30 PM
Keywords
Environment, Sustainability, World Perspective
Abstract
The city of Manaus in Amazonia was the center of the 19th century rubber boom. Becoming the richest city of the Amazon, Manaus enjoys a rich history and expanding population. This study aims to show how the physical landscape has changed between 1986 and 2001 using Object-Oriented Image Segmentation and Classification with Landsat5-7 imagery. The process of classification works to determine land features (e.g. urban, forest, field, water, etc...) and can be used to find trends and statistics of land cover/land use change. The image data will be georectified and have atmospheric effects removed. The Object-Oriented Image Segmentation and Classification works by analysis of image objects and spatial relationships instead of on single pixels (i.e. traditional image classification). By creating rule sets, land cover can be extracted and classified more accurately.
Land Use/Land Cover Change in Manaus, Brazil
Kirkhof Center 2270
The city of Manaus in Amazonia was the center of the 19th century rubber boom. Becoming the richest city of the Amazon, Manaus enjoys a rich history and expanding population. This study aims to show how the physical landscape has changed between 1986 and 2001 using Object-Oriented Image Segmentation and Classification with Landsat5-7 imagery. The process of classification works to determine land features (e.g. urban, forest, field, water, etc...) and can be used to find trends and statistics of land cover/land use change. The image data will be georectified and have atmospheric effects removed. The Object-Oriented Image Segmentation and Classification works by analysis of image objects and spatial relationships instead of on single pixels (i.e. traditional image classification). By creating rule sets, land cover can be extracted and classified more accurately.