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Development of a Methodology for Predicting Forest Area for Large-Area Resource MonitoringAuthor(s): William H. Cooke
Source: Res. Pap. SRS-24.Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 16p.
Publication Series: Research Paper (RP)
Station: Southern Research Station
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DescriptionThe U.S. Department of Agriculture, Forest Service, Southcm Research Station, appointed a remote-sensing team to develop an image-processing methodology for mapping forest lands over large geographic areds. The team has presented a repeatable methodology, which is based on regression modeling of Advanced Very High Resolution Radiometer (AVHRR) and Landsat Thematic Mapper (TM) data. It is a methodology that Forest inventory and Analysis (FIA) survey personnel can implement in any region or area. The term repeatable implies objectivity. Studies in the conterminous United States, Central America and Mexico, and west Texas and Oklahoma have provided valuable insights that address the subjective nature of some of the steps taken in mapping large forest areas. The team has identified seven such steps. They have reduced or eliminated subjectivity in four of the steps and identified two steps in which objectivity can be enhanced.
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CitationCooke, William H. 2001. Development of a Methodology for Predicting Forest Area for Large-Area Resource Monitoring. Res. Pap. SRS-24.Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 16p.
KeywordsAVHRR, ecoregions, FIA, Landsat, regression modeling, remote sensing
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