The Forest Inventory and Analysis program (FIA) of the U.S. Forest Service monitors status and trends in forested ecoregions nationwide. The complex nature of this broad-scale, strategic-level inventory demands constant evolution and evaluation of methods to get the best information possible while continuously increasing efficiency. In 2004, the "Nevada Photo-Based Inventory Pilot" (NPIP) was launched and involved the acquisition and processing of large-scale aerial photography (LSP) throughout the State of Nevada. The over-arching goals of this pilot are to exceed information requirements, accelerate inventory timelines, and reduce inventory costs. Meeting these objectives requires the development of several complex and inter-related procedures, including photo-sampling protocol, statistical estimators, cover measurement techniques, and improved methods for mapping forest and nonforest attributes. This report documents the first of these procedures, the photo-sampling protocol for the NPIP project.
Foresters are increasingly interested in remote sensing data because they provide an overview of landscape conditions, which is impractical with field sample data alone. Light Detection and Ranging (LiDAR) provides exceptional spatial detail of forest structure, but difficulties in processing LiDAR data have limited their application beyond the research community. Another obstacle to operational use of LiDAR data has been the high cost of data collection. Our objectives in this study were to summarize, at the stand level, both LiDAR- and Landsat (satellite)-based predictions of some common structural and volume attributes and to compare the cost of obtaining such summaries with those obtained through traditional stand exams. We found that the accuracy and cost of a LiDAR-based inventory summarized at the stand level was comparable to traditional stand exams for structural attributes. However, the LiDAR data were able to provide information across a much larger area than the stand exams alone.
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