Since the early 1900s, forest inventory has been used in the United States to estimate stand parameters and conditions to facilitate the development of short- and long-term management strategies. Large-area forest inventory has gradually evolved away from a labor-intensive and time-consuming process with slow data collection and processing and difficult forest stand delineation.
Today, remote sensing and Geographic Information System (GIS) technology are increasingly used by forest resource managers and users to support planning initiatives. These tools allow forest inventories to be completed in a much more timely manner with greater accuracy and provide a database for storing, manipulating, and displaying spatial data often missing in more traditional inventories.
This study evaluates the application of remote sensing and GIS technology to inventory eastern white pine (Pinus strobus L.) over a 21-county area in eastern Ohio. Because of its potential for rapid growth on a wide variety of sites, white pine has been planted extensively on abandoned farmland and strip mines in Ohio since the 1920s. Despite this, white pine has remained an underdeveloped, essentially unrecognized, resource in the state. Certainly a major contributing reason for this is a lack of information on the amount, age, size, and distribution of the resource.
Currently, the only information on the extent and distribution of white pine in Ohio is the Forest Inventory and Analysis (FIA) conducted by the U.S. Forest Service in 1991. According to the FIA, an estimated 61,000 acres of white pine are distributed throughout the state, with nearly 70 percent of the total volume concentrated in 18 counties located in the East-Central and Southeastern Units (Griffith et al., 1993). However, the sampling error associated with these volume estimates is 57.9% in the Southeastern Unit and 34.5% in the East-Central Unit.
A more precise and reliable inventory is required before the economic potential of Ohio's white pine can be evaluated and suitable management and utilization strategies developed. An inventory that utilizes traditional techniques would be time consuming and expensive, requiring extensive field sampling and exhaustive hours studying aerial photographs and conducting ground surveys. However, the use of remote sensing and GIS technology should be ideally suited to complete such an inventory more rapidly and with improved sampling error. The primary objectives of this study were, therefore, to:
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