Sample design

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User's guide to PlanWise

Prior to inventory, a "stratification" needs to be done. This is valid also in a complete inventory (when all stands in the analysis area are included), since some general inventory parameters are set in this part. Considering cost-efficiency, the inventory is commonly based on a two-stage sampling procedure. Here, this starts with selecting the imported stand register of interest, i.e. the analysis area to be in-field inventoried by plotwise sampling in PPS-sampled stands.


  1. In PlanWise, open an existing project or create a new one.
  2. From the top menu "Data Management" (Datahantering), select "Sample Design" (Stickprovsdesign).
  3. Click on "Create New".
  4. A wizard will guide you through the setup of a sample design. The steps are explained in more detail below. In short:
    1. Select Stand Register: Select what stand register on which to base the sample design. Edit the sample design under "Name of Sample Design" or leave as it is. Click "Next".
    2. Create Domains: If you want, you can add domains with "Add Domain" (see below). Click "Next".
    3. Create Strata: Click "New" to add a new stratum.
    4. To add stands to a stratum, select one or more cells in the matrix, and click "Add Selected". You can hold down the Ctrl-key, and select cells one by one, or press the left mouse button and select blocks of cells. A stratum must first have been created before you can add stands to it. ## After all cells have been assigned a stratum, you will be able to continue by clicking "Next".
    5. Sample Size: Select the numner of sample stands, and allocate stands to stratum. You can let the program propose an allocation, and edit this one manually.
    6. Sample Plot Intensity: The number of sample plot per sample stands can be a function of selected stand variables.
    7. Sample Plot Radius: The plot radius for established forest can be set as a function of selected stand variables. The plot radius for young forests (plant plot, regeneration plot) is set at the bottom of the page (Plant Plot Radius).
    8. Sample Trees: P1, P2, and P3 are parameters used for selecting sample trees. See below.
    9. Leave "Automatically generate new seed" unchecked, if you want to be able to repeat the procedure and obtain the same sample result.
    10. Summary: Display the sample list of stands to inventory. Inspect!

Stand Data Required

The following stand register parameters are required to enable stratification:

  • Area (productive forest land),
  • Volume
  • Age
  • Basal area
  • Number of stems
  • Tree species proportions


A domain is here a certain type of forest (e.g., stands with the dominating tree species Pinus contorta) where a certain inventory-scheme should be applied, e.g. securing a certain sample size. The classification is done in the same manner as the forest domain-builder in PlanWise, i.e. the user can select stand-wise properties in the register and define (by using the logical operators) the conditions of a certain domain. If the same inventory-scheme should be applied in all stands in the current analysis area, no additional domains needs to be defined.


The stratification is done in each domain separately. The stands (their productive forest land areas) of a certain domain are related to one of the several user-defined strata. In addition to total area, the number of stands and the area of the largest stand can be viewed in each stratum. The stratum-matrix should first be arranged according to current forest state (by number of classes and class width) and, if desirable, defined by other variables than volume and age.

Number of Sample Stands

This is called the sample size-definition part. First, a defined number of sample stands will be allocated to each stratum (e.g., proportional to the volume). You could and probably should re-distribute the sample stands over the different strata, with a higher sampling intensity in strata with more valuable forests and where forest management decisions are supposed to be more difficult. The term and parameter "Representative Area", with a value calculated as Productive forest land area in current stratum / Sample size (number of sampled stands) in current stratum, might be found a bit confusing. A scale factor with a unit (hectares)? With the same value for all sampled stands in a stratum (despite they probably will have different areas)? Don't worry, as long as the sampling of stands is done PPS, and size is the productive area (in hectares), it will all turn out alright. Try to follow this: an estimation of, e.g., the total biomass (in m3<i) defined as (Area of the i:th sample stand * Number of sample stands in current stratum) / Area of current stratum. By dividing this estimation by the total area of the stratum an average value (here in m3/ha) is derived. Finally, the total volume of biomass in current stratum that is represented by the sample is derived by multiplying the average value-estimate by Representative area. The only time this procedure is not followed is when a single stand show an area equal to or greater than the representative area - such stand will become a sample stand and represent its own area (nothing more, nothing less).

NB: There should be at least three sample stands in each stratum. Moreover, aviod extreme differences in the representative area of different strata.

Plot Intensity

The presupposed number of plots in a sample stand is set, depending on stand characteristics (chosen by the user). This is called the sample plot intensity. The matrix can be re-arranged as in previous parts, by selecting preferred variables and number of classes and class width.

Plot Size

Definitions corresponds to the radius, in meters, of the circular plots. Finally, some additional settings are made, necessary in the inventory (e.g. in the sample tree sampling). Read more about the sample tree parameters 1-3 here. Unless you're into "living on the edge", it is recommended you leave P1 and P3 as default and later (in field) edit P2 to obtain a certain number of sample trees per plot (on average).

NB: The number of plots per stand is set considering costs and assumed losses (caused by bad decisions made from erratic forest descriptions), less than three is only considered stupid. Unless they are very large, with radius >>10 m (and in small stands, with area <0.5 ha). A corresponding line of reasoning is held for the number of sample trees per plot, where less than 0.5 is stupid. The plots are circular, however when, e.g., acting as reference ("ground truth") to remotely sensed data with certain (quadratic) pixel size, you should adjust the plot radius hereof and define radii in meters with two decimals.

This part is finished by actually sampling the sample stands in each stratum (done PPS where size is the stand area)

Inspect the sampled sample stands regarding, e.g., the total number and each stands corresponding stratum. When values in corresponding columns (e.g. P1-P3, Stratum, Plots, and Radius) looks ok, you can finish and lock the sample.

Knowing the size of the sample stands (A, the area in m2) and the corresponding number of plots (n), the plot positions are derived externally. Company specific GISs are used to obtain "Theoretical" x- and y-coordinates (in meters, preferrably in RT90) for each plot in each sample stand. The actual number of plots in a stand can differ from the presupposed, since the layout is randomized*. The actual plot positions, "True" coordinates, are obtained from GPS, preferrably placed at plot centre and averaging positions made during field work. Post-processing of GPS-data will improve the accuracy of the positions even further. Advantages in using external GIS, and perhaps external GPSs too, are numerous; e.g. facilitating navigation in field to stands and plots, using suitable images (e.g. ortophotos) and maps (possible paper print-outs).

*) Within a sample stand, the location of the first plot is randomly chosen, and from this all other plots are systematically located in a square lattice (with spacing (in m) s = (A/n)**0.5). This simplifying the navigation between plots and, above all, increasing the accuracy in the estimates.