[PDF] Guidelines For The Use Of Digital Imagery For Vegetation Mapping eBook
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A basic reference for those considering digital imagery, particularly satellite imagery for vegetation mapping. Contents: using remote sensing and GIS for mapping vegetation; remote sensors and remotely sensed data; determining appropriate uses for satellite imagery; defining the classification scheme; collecting reference data; assessing accuracy; creating polygons; project management; the basic tour; and case studies. Important terms and ideas are introduced while showing the progression of key activities in the classification and mapping process.
Softcopy photogrammetry has proven useful to reduce mapping time with aerial photography and aids in producing a digital product that is easily transferable over other electronic media. This study brings together computer stereo viewing with scanned aerial photos in a GIS to produce a fully digital protocol for mapping vegetation to the formation level. Erdas Imagine was used to generate digital images from aerial photos, Erdas Orthobase was applied to orthorectify the images through a joint triangulation solution for 42 photos, and Erdas StereoAnalyst provided on screen stereo viewing for vegetation delineation. Vegetation polygons were then classified using the National Vegetation Classification System formations in ArcView 3.2, and a thematic accuracy assessment was carried out on the vegetation map using the USGS-NPS standards. A positional accuracy assessment was conducted on the photo mosaic produced from the orthorectified images. Thematic accuracy was 77.55% initially, and the revised map had an 88.70% thematic accuracy. Positionally, the photo mosaic had Class 1 positional accuracy along the X-coordinate with 0.603 meters RMSE and had Class 2 accuracy along the Y-coordinate with 2.415 meters RMSE. A protocol using entirely digital methods was produced with the software cited that meets the formation level USGS-NPS vegetation mapping standards.
In recent years, mapping software utilizing scanned--or "softcopy"--Aerial photographs has become widely available. Using scanned photos of Valley Forge (PA) National Historical Park, I explored some of the latest tools for image processing and computer-based vegetation mapping. My primary objective was to compare different approaches for their efficiency and accuracy. In keeping with the USGS-NPS Vegetation Mapping Program protocol, I classified the park's vegetation according to The Nature Conservancy's National Vegetation Classification System (NVCS). Initially, I scanned forty-nine 1:6000 color-infrared air photos of the area at 600 dpi using an Epson desktop scanner. I orthorectified the images by two different methods. First, I did so on a single-image basis using ERDAS Imagine. In this approach, United States Geological Survey (USGS) Digital Ortho Quarter Quadrangles (DOQQ) and a 10-meter Digital Elevation Model (DEM) served as references for between seven and twelve ground control points per photo. After achieving a root mean square error (RMSE) of less than 1 meter for an image, I resampled it into an orthophoto. I then repeated the process using Imagine Orthobase. Via aerial triangulation, Orthobase generated an RMSE solution for the entire block of images, which I resampled into orthophotos using a batch process. Positional accuracies were remarkably similar for image mosaics I created from the single-image as well as the Orthobase orthophotos. For both mosaics, planimetric x-coordinate accuracy met the U.S. National Map Accuracy Standard for Class 1 maps, while planimetric y-coordinate accuracy met the Class 2 standard. However, the Orthobase method is faster--reducing process time by 50%--and requires 20% (or less) of the ground control points necessary for the single-image method. I delineated the park's vegetation to the formation level of the NVCS. Using ESRI ArcMap, I digitized polygons of homogeneous areas observed from the ort.