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| + | #REDIRECT[[:Category:Plot design]] |
| − | The response design defines what is being done once a sampling element is selected. In [[forest inventory]], it makes sense to speak of “plot design” as we usually select [[Sample plot|sample plots]] or more general of “observation design”.
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| − | There are 4 basic types of observation units:
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| − | #individual objects, such as [[Tree Definition|trees]], [[Forest Definition|stands]], properties; on individual objects any attribute can be observed; | + | |
| − | #(dimensionless) points: a typical application are [[dot grids]]. Only limited categorical variables can be observed on dimensionless points, either dichotomous variables (for example forest/non-forest) or categorical variables with more than two classes, such as [[Forest Definition|forest type]].
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| − | #(one-dimensional) lines: on a [[line sampling|sample line]] it can be observed how many intersections there are with line features (roads, forest edge, …) or which portion of the sample line is in a particular forest type.
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| − | #(two-dimensional) areas: such as [[fixed area plots]]. On the set of objects (trees) found on that area, attributes are being observed.
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| − | While all of these types of observation units are found in forest inventory, the sample plots are probably the most frequently used.
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| − | In fact, all the basic plot types ([[fixed area plots]], [[Bitterlich sampling|relascope plot]], [[distance based plots]]) can be uniformly characterized as follows: the [[:category:sampling design|sampling design]] defines how sample points are being selected in the (forest) area of interest. Once a sample point is selected, the plot design defines how to select around that point the sample trees that are to be included in order to obtain an observation. For fixed area plots, some geometric shape is defined around the sample point and all trees within this shape are observed; in relascope sampling all those trees are taken as sample trees that appear wider than a defined opening angle with which the surrounding trees are aimed at; and in [[distance based plots]] a fixed number of k nearest trees is taken as sample trees in each sample point.
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| − | That is, the plot design implies defining how to select the sample trees to be measured around each sample point.
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| − | It is important to recall the factors [[Random selection|randomization]] and [[sample size]]: each randomization increments the sample size by 1. That is, when we select sample plots at random, then the number of sample plots is the sample size and not the number of trees that are measured on these plots!
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| − | Measurement procedures must be defined for all variables that are measured on the plot. When doing the measurements, care must be taken, that no tree is forgotten and that no tree is measured twice. It helps to mark the measured trees with chalk or other color, where it is recommendable to mark it such that it can be well seen from the center point (in circular plots). In strip plots this is usually not a problem as one proceeds on the central line step by step.
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| − | Temporary plots and [[Estimation_on_changes#Permanent_plots|permanent plots]] can be distinguished. Temporary plots are visited once. Permanent plots are visited in regular intervals. Therefore, permanent plots need to be prepared such that they can be easily found on the next inventory and that future measurement can straightforwardly be taken.
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| − | |keywords=individual objects,points,lines,areas,spatial autocorrelation,plot design,forest inventory,
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| − | |descrip=The plot design defines what is being done once a sampling element is selected.
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| − | [[Category:Plot design]]
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