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Comparison of High Spatial Resolution Imagery for Efficient Generation of GIs Vegetation Layers

2000; American Society for Photogrammetry and Remote Sensing; Volume: 66; Issue: 11 Linguagem: Inglês

2374-8079

Lloyd L. Coulter, Douglas A. Stow, Allen Hope, John F. O’Leary, Debbie Turner, Pauline Longmire, S. Peterson, John Kaiser,

Species Distribution and Climate Change

naditional vegetation mapping approaches require extensive field reconnaissance, and normally involve delineation of vegetation boundaries through interpretation of aerial photographs. In order to generate vegetation data layers within a geographic information system (GIS), boundaries must be digitized and georeferenced and vegetation attributes coded. A project - conducted through the National Aeronautics and Space Administration Affiliated Research Center at San Diego State University for Ogden Environmental and Energy Services, Inc. - investigated the utility of very high spatial resolution (1-m) digital multispectral image data for generating GIS vegetation layers. Mapping and digital encoding of vegetation polygons was performed using USGS color-infrared [CIR) digital orthophotographic quarter quadrangle [DOQQI and Airborne Data Acquisition and Registration [ADAR) 5500 imagery. The two data sources were compared in the context of a controlled experiment which tested the utility of the imagery under multiple mapping scenarios. The study area was a habitat reserve within Marine Corps Air Station Miramar near San Diego, California. This area primarily supports shrubland vegetation types typical of the Mediterranean climate area of southern California. CIR image data derived directly from multispectral digital cameras [e.g., ADAR System 5500) enabled more accurate classification and mapping of vegetation than did digital imagery generated from scanned ~IR aerial photographs. This result is largely attributed to the higher spectral and radiometric fidelity of direct digital capture, but may also be attributed to more optimal seasonality for the data of the ADAR acquisition. While the mapping was based upon interactive, visual image interpretation and on-screen digitizing, the following image processing techniques proved to be helpful for aiding interpretation: contrast enhancement prior to generating hardcopy prints for field analyses and during on-screen classification and digitizing, per-pixel image classification based on spectral-radiometric pattern recognition, and derivation of normalized difference vegetation index maps. The overall accuracy of interpreter-derived vegetation maps was approximately 75 percent for the entire study area.