Abstract
Habitat mapping can be accomplished using many techniques and types of data. There are pros and cons for each technique and dataset, therefore, the goal of this project was to investigate the capabilities of new satellite sensor technology and to assess map accuracy for a variety of image classification techniques based on hundreds of field-work sites. The study area was Masonboro Island, an undeveloped area in coastal North Carolina, USA. Using the best map results, a habitat change assessment was conducted between 2002 and 2010. WorldView-2, QuickBird, and IKONOS satellite sensors were tested using unsupervised and supervised methods using a variety of spectral band combinations. Light Detection and Ranging (LiDAR) elevation and texture data pan-sharpening, and spatial filtering were also tested. In total, 200 maps were generated and results indicated that WorldView-2 was consistently more accurate than QuickBird and IKONOS. Supervised maps were more accurate than unsupervised in 80% of the maps. Pan-sharpening the images did not consistently improve map accuracy but using a majority filter generally increased map accuracy. During the relatively short eight-year period, 20% of the coastal study area changed with intertidal marsh experiencing the most change. Smaller habitat classes changed substantially as well. For example, 84% of upland scrub-shrub experienced change. These results document the dynamic nature of coastal habitats, validate the use of the relatively new Worldview-2 sensor, and may be used to guide future coastal habitat mapping.
Highlights
Barrier islands exist along much of the coastline along the Eastern United States, and are home to many species of flora and fauna
A total of 44 classification maps were generated for the larger National Estuarine Research Reserve System (NERRS) study area and 124 maps were generated for just the Masonboro Island area
All maps were assessed for classification accuracy using half the Ground Reference Points (GRPs) that were collected for each habitat class
Summary
Barrier islands exist along much of the coastline along the Eastern United States, and are home to many species of flora and fauna. These areas are constantly undergoing geomorphic change due to wind and water stresses that alter their size and orientation [1,2]. WorldView-2, QuickBird, and IKONOS satellite imagery and. Light Detection and Ranging (LiDAR) elevation and texture data were tested for mapping the study area. The primary objectives of this study were to determine the accuracy of several different satellite sensors and image processing methods for mapping coastal vegetation and to assess how the island has changed over time. The methodologies developed can be implemented to study other coastal locations
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