Sunday, 6 January 2013
Exhibit Hall 3 (Austin Convention Center)
In this study we use very high resolution QuickBird imagery of Wallops Island, Virginia and image processing tools available via ENVI Image Analysis Software to classify vegetation types, water, land, beach and man-made materials. We will use two methods to classify the images: 1) Simple Ratio (SR) and 2) Normalized Difference Vegetation Index (NDVI). The simple ratio is SR = (ρ1/ρ2) where ρ1 is red reflected radiant flux associated with the red band, and ρ2 is the near-IR radiant flux associated with the near-IR band. The simple ratio gives the Leaf Area Index (LAI), which will yield different values of biomasses, and will help in determining different types of vegetation. The NDVI = (ρ2 - ρ1) / (ρ2+ρ1). This method allows one to see the seasonal changes over time as well as reducing noise from the image. The classified images will be compared to a digital terrain model (DTM) developed from LiDAR data. The DTM will allow us to identify and quantify low-lying areas prone to flooding from sea level rise. The QuickBird classified images will provide information on habitats and types of wetlands that will be affected by sea level rise. Our goal is to assess the effects of sea level rise on pristine wetlands that sustain the habitats including endangered birds and other wildlife in the Delmarva region.
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