Spatial Variability of Leaf Area Index in Homogeneous Forests Relates to Local Variation in Tree Characteristics
Forest canopy density can be highly variable within one stand. The accuracy of indirect methods to quantify stand leaf area index (LAI) is often unknown, and intensive sampling strategies are required. Our objectives were to study the drivers of the spatial LAI variability and to improve the sampling strategy based on a sampling protocol as a function of the local canopy pattern. We examined the spatial variability of hemispherical photography (HP)-based LAI estimates of European beech (Fagus sylvatica L.), pedunculate oak (Quercus robur L.), and Scots pine (Pinus sylvestris L.) in Flanders, Belgium. Within the 30 selected forest stands, a regular grid of 16 LAI measurement points and a circular forest inventory plot were established. The LAI estimates of the grid points were used to calculate the LAI of the squared cells (defined as “patches”), within the regular grid. Local forest inventory data were used to study the drivers of the deviation of patch LAI (LAIdev) relative to the average plot LAI. Average tree distance from a patch center was negatively related with the LAIdev. Tree structural characteristics (dbh, tree height, crown length, and crown cover) were all positively related to the LAIdev. Based on our findings, we suggest that for the forest types analyzed, sampling layouts for HP-based LAI estimates should follow a pattern of selecting two (beech and pine) or three (oak) sample points and positioning the camera at a distance of approximately 20% of the dominant height from one (beech) or two (oak and pine) large trees.
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Document Type: Research Article
Publication date: 2012-12-02
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- Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
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