John A. Kershaw

Influence of canopy structure assumptions on predictions from Beer's law. A comparison of deterministic and stochastic simulations

Abstract Since Monsi and Saeki introduced the use of Beers law to describe light extinction in plant canopies, this law has experienced wide use. Many authors have proposed models for describing foliage distribution within the canopy, with many different crown distribution assumptions. This paper explores seven canopy models and the consequences of these canopy models on prediction of light extinction. These seven canopy models range from three-dimensional homogeneity of the entire canopy space to a three-dimensional crowns predicted for each tree. Light extinction predictions using both deterministic and stochastic methods allow examination of different sources of variation within the canopy. Average light extinction at the base of the canopy was found to be equivalent for all models and assumptions. The models varied markedly, however, in the pattern of the average light extinction within the canopy, as well as in the variation expressed at specific points within the canopy. Discussion of the implications of these simulations, given the various assumptions and objectives, provide some suggestion as to the appropriate use of Beers law.

Layer Stacking: A Novel Algorithm for Individual Forest Tree Segmentation from LiDAR Point Clouds

Abstract As light detection and ranging (LiDAR) technology advances, it has become common for datasets to be acquired at a point density high enough to capture structural information from individual trees. To process these data, an automatic method of isolating individual trees from a LiDAR point cloud is required. Traditional methods for segmenting trees attempt to isolate prominent tree crowns from a canopy height model. We here introduce a novel segmentation method, layer stacking, which slices the entire forest point cloud at 1-m height intervals and isolates trees in each layer. Merging the results from all layers produces representative tree profiles. When compared to watershed delineation (a widely used segmentation algorithm), layer stacking correctly identified 15% more trees in uneven-aged conifer stands, 7%–17% more in even-aged conifer stands, 26% more in mixedwood stands, and 26%–30% more (with 75% of trees correctly detected) in pure deciduous stands. Overall, layer stackings commission error was mostly similar to or better than that of watershed delineation. Layer stacking performed particularly well in deciduous, leaf-off conditions, even those where tree crowns were less prominent. We conclude that in the tested forest types, layer stacking represents an improvement in segmentation when compared to existing algorithms.

Using Stocking Guides to Take Stock of Forest Institutions

Policy analysts have struggled to find methods that allow meaningful evaluation of forest management institutions, such as forest-sector decentralization, in terms of the resulting forest conditions. This article demonstrates how to use stocking indices to measure forest conditions when comparing forests across time or space. These measures are especially useful for comparative institutional and policy analysis because they are constructed with a common scale, thus providing a meaningful metric by which to compare conditions. As an example of the application of the method, evidence from Uganda is presented to show a reduction in stocking in many types of Ugandan forests during a time period of forest sector decentralization.