FOREST COVER TYPE ANALYSIS OF NEW ENGLAND FORESTS USING INNOVATIVE WORLDVIEW-2 IMAGERY

摘要

For many years, remote sensing has been used to generate land cover type maps in an attempt to create a visual representation of what is occurring on the ground, specifically for identifying forest types. New England forests are notorious for their especially complex forest structure and as a result have been, and continue to be, a challenge when classifying forest cover. In order to most accurately depict land cover classifications occurring on the ground, it is essential to utilize data that has a suitable combination of both spectral resolution and spatial resolution. The WorldView-2 commercial satellite, launched in 2009, is the first of its kind, having both high spectral and spatial resolution. It records eight bands of multispectral imagery, that is four more than the usual high spatial resolution sensors, and has a pixel size of 1.85 meters. These additional bands may improve class detail and classification accuracy of land cover maps, specifically for vegetation, and thus the creation of forest type maps. In keeping with recent developments in image analysis, an object-based image analysis approach was used to classify forest cover in Pawtuckaway State Park and nearby private lands, an area representative of the typical complex forest structure found in the New England region. The improved spectral and spatial resolutions of the WorldView-2 imagery was utilized, both by itself and in combination with Landsat multispectral imagery, to evaluate whether they could more accurately classify the forest cover types. More specifically, Classification and Regression Trees (CART) analysis was used to classify these forest cover types. Accuracies for each forest type map produced were generated using error matrices and additional standard accuracy measures (i.e. KAPPA) were generated as well. The results from this study verify the value of analyzing imagery with both high spectral and spatial resolutions, and the usefulness of Worldview-2's new and innovative bands for the classification of complex forest structures.