The Angle-Count Method

Holgate, P.

doi:10.2307/2335052

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…An automated, instrumentbased technique has the potential to augment conventional manual techniques particularly in terms of objectivity and repeatability of results and time saving. Inventory samples are often conducted in fixed-size plots, but may also apply statistical selection methods such as the Bitterlich or angle count method which is often undertaken using a relaskop instrument (Holgate, 1967;Bell and Dilworth, 1998). In managed plantations, there are often well established relationships between structural variables (allometry) and these allow additional parameters to be estimated from those that are measured.…”

Section: Introductionmentioning

confidence: 99%

Measuring tree stem diameters using intensity profiles from ground-based scanning lidar from a fixed viewpoint

Lovell

Jupp

Newnham

et al. 2011

ISPRS Journal of Photogrammetry and Remote Sensing

149

103

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Section: Introductionmentioning

confidence: 99%

Measuring tree stem diameters using intensity profiles from ground-based scanning lidar from a fixed viewpoint

Lovell

Jupp

Newnham

et al. 2011

ISPRS Journal of Photogrammetry and Remote Sensing

149

103

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“…6, drawn from our first simulation with a stem density of 784 stems per hectare and a plot centre coordinate of (1, 1). Holgate (1967) gives the estimate variance of a single angle count VarEST(G) as GK, which requires knowledge of the true basal area. Given the imprecision in G, using its value as an estimate of the true G is unlikely to give useful variance estimates.…”

Section: Discussionmentioning

confidence: 99%

Improved estimates of per-plot basal area from angle count inventories

Eastaugh¹,

Hasenauer²

2014

iForest

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© iForest -Biogeosciences and Forestry IntroductionBasal area is a key descriptor of a forest stand, and is often used to estimate other forest attributes such as biodiversity indices (Motz et al. 2010), as a key parameter of ecological habitat models (Zielinski et al. 2006, Floyd et al. 2009) or as validation data for large scale ecosystem models (Bugmann & Solomon 2000). National Forest Inventories comprise large bodies of individual tree measurements, usually derived from intensive field sampling. Such inventories were traditionally designed to efficiently estimate the standing stock of timber available in large spatial areas, but this data is increasingly being used in other, more ecologically based applications. However, for inventories that use the common "sampling proportional to size" design (angle-count plots -Bitterlich 1948) the data available for individual plots is a point estimate (not a spatially explicit measurement as available from fixed-area plots). Large numbers of angle-count plots must thus be aggregated to attain an adequate estimate of mean basal area. This is problematic in applications where the data from each single plot is assumed to be a reasonably accurate representation of conditions on that plot and regressed against some prediction or dependent variable. This problem is particularly acute when angle-count inventory data is used to evaluate the performance of forest growth models (i.e., , Huber et al. 2013; while the full inventory (aggregating all points) may be ideal for assessing model bias, the uncertainty of the individual plot-level data makes evaluation of model precision impossible. We present here a new method of obtaining more useful estimates of basal area on single angle-count plots through the resampling of a National Forest Inventory (NFI) database.When inventory data is used for its designed purpose (the assessment of mean basal area or volume over large areas) the imprecision of individual plot data is not relevant, as (presumably) the sample design of the inventory uses sufficient plots to reduce standard errors to useful limits. Recently however large-scale inventory data is being used in carbon budget studies (i.e., Bellassen et al. 2011, Mohren et al. 2012, to derive estimates of historic forest management practices , to model forest growth dynamics (Didion et al. 2009, Lichstein et al. 2010, to study forest damage (Jalkanen & Mattila 2001) and in a host of other applications. Mäkelä et al. (2012) have pointed out the advantages of using permanent plot NFI records for model development and calibration, highlighting the broad-scale representativeness of the datasets.Sophisticated forest models usually have intensive data requirements, and NFI data is potentially an extremely valuable resource. Many models can be run in a point-based, scale indeterminate fashion to avoid the assumption that single-point data is fully representative of an area (Seidl et al. 2013), but the non-linear nature of many modelled processes means that imprecision in the input data can lea...

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The Angle-Count Method

Cited by 2 publications

References 2 publications

Measuring tree stem diameters using intensity profiles from ground-based scanning lidar from a fixed viewpoint

Measuring tree stem diameters using intensity profiles from ground-based scanning lidar from a fixed viewpoint

Improved estimates of per-plot basal area from angle count inventories

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