|
Keywords
|
Massachusetts, forest cover change
mapping, pixel-based image analysis, image segmentation, object based
classification
|
|
Abstract
|
The Massachusetts landscape has
continually undergone natural and anthropogenic change since the arrival of
European settlers. The long-term history of statewide spatio-temporal forest
cover dynamics is responsible for creating a unique landscape. Forest regeneration is
occurring across the state, primarily in former agricultural areas. Current
timber harvest occurs in non-industrial private forests (NIPFs) and in
peri-urban areas. Remote sensing data and techniques provide an effective
means to assess land cover change over time. This paper presents the results
of a comparison between forest cover change mapping using pixel-based image
analysis and classification using image segmentation and subsequent object
based image classification, employing Landsat TM and ETM imagery. The study
area is the North Quabbin region of Massachusetts, between 1989 and 1999.
Traditional forest cover change mapping utilizes pixel-based analysis of
imagery. This relies on classifications of individual pixels carrying
integrated spectral signals treated as individual samples, clustered in a
multi-dimensional feature space. Imagery and research demands are not always
met using pixel-based classification. Using image segmentation and object
based classification explores image segments as spatially contiguous and
spectrally homogenous groups of pixels. Homogeneous groups of pixels combine
the spectral behavior of geographic features with spatial characteristics.
Comparison results show that using image segmentation and object based image
classification for forest cover change mapping of Landsat TM and ETM imagery
is more effective than using traditional pixel-based image analysis. With
multi-resolution imagery, object-based classification utilizing spectral and
geometrical properties, as well as object relationships, is more appropriate
and accurate in assessing forest cover change.
|