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Modeling variability and scale integration of LAI measurementsAuthor(s): Kris Nackaerts; Pol Coppin
Source: In: Hansen, Mark; Burk, Tom, eds. Integrated tools for natural resources inventories in the 21st century. Gen. Tech. Rep. NC-212. St. Paul, MN: U.S. Dept. of Agriculture, Forest Service, North Central Forest Experiment Station. 392-398.
Publication Series: General Technical Report (GTR)
Station: North Central Research Station
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DescriptionRapid and reliable estimation of leaf area at various scales is important for research on chance detection of leaf area index (LAI) as an indicator of ecosystem condition. It is of utmost importance to know to what extent boundary and illumination conditions, data aggregation method, and sampling scheme influence the relative accuracy of stand-level LAI measurements. This knowledge should lead to a high repeatability and relative accuracy of the LAI measurements. In this research, LI-COR is recorded with a Licor LAI-2000, one of the more modem and widely used plant canopy analyzer instruments. The impact of external factors (boundary and illumination conditions) is minimized by means of a viewcap. The impact of sampling scheme and data aggregation method on the relative accuracy of the retrieved stand-level LAI value is quantified by means of Monte Carlo simulation.
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CitationNackaerts, Kris; Coppin, Pol. 2000. Modeling variability and scale integration of LAI measurements. In: Hansen, Mark; Burk, Tom, eds. Integrated tools for natural resources inventories in the 21st century. Gen. Tech. Rep. NC-212. St. Paul, MN: U.S. Dept. of Agriculture, Forest Service, North Central Forest Experiment Station. 392-398.
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