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Application of geostatistics to characterize leaf area index (LAI) from flux tower to landscape scales using a cyclic sampling design

Document Type

Article

Journal/Book Title/Conference

Ecosystems

Volume

5

Issue

7

First Page

667

Last Page

679

Publication Date

2002

Abstract

Accurate characterization of leaf area index (LAI) is required to quantify the exchange of energy, water, and carbon between terrestrial ecosystems and the atmosphere. The objective of this study was to use a cyclic sampling design to compare the spatial patterns of LAI of the dominant terrestrial ecosystems that comprised the area around the 447-m WLEF television tower, equipped with an eddy flux system, near Park Falls, Wisconsin, USA. A second objective was to compare the efficiency of cyclic, random, and uniform sampling designs in terms of the precision of spatial information derived per unit sampling effort. The vegetation surrounding the tower was comprised (more than 80%) of four major forest cover types: forested wetlands, upland aspen forests, upland northern hardwood forests, and upland pine forests, and a fifth, nonforested cover type, grass (open meadow). LAI differed significantly among the five cover types and averaged 3.45, 3.57, 3.82, 3.99, and 1.14 for northern hardwoods, aspen, forested wetlands, upland conifers, and grass, respectively. The cyclic sampling design maximized information about the variance of vegetation characteristics of the heterogeneous landscape and decreased by 60% the number of plots needed to obtain the same confidence interval width using a random sampling design. The range of spatial autocorrelation for LAI was 147 m, but it was decreased to 117 m when vegetation cover was included as a covariate. The cyclic sampling design has several important advantages over other sampling designs. The cyclic sampling design increased the sampling efficiency by optimizing the placement of plots so they were distributed more efficiently for geostatistical analyses such as semi-variograms, correlograms, and spatial regression and can incorporate covariates (for example, vegetation cover, soil properties, and so on) that may explain the sources of spatial patterns. The cyclic sampling design was used to derive a spatial map of LAI and the average LAI for the 3 × 2 km area centered on the flux tower was 3.51 ± 0.89 (with a minimum of 0 and a maximum of 6.35). Airborne and satellite reflectance data have also been used to characterize LAI, but in this region, and many other forests of the world, remotely sensed vegetation indexes saturate in forests with an LAI greater than 3–5. The cyclic sampling design also provides a general ecological sampling approach that can be used at multiple scales.

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