+64 7 350 3209 +61 2 8011 3645 info@interpine.co.nz

Forest Inventory – Key Planning Components

by | Feb 5, 2015 | Forest Inventory, GIS and Mapping | 0 comments

At Interpine we offer a wide range of forest inventory products, all with same goal; to provide accurate data to decision makers.   Here is a few tips to consider on this pathway to an accurate and precise final dataset.

Outline Objectives

Before starting the process of inventory planning a key component is to outline the inventory objectives since these assumptions define how the data is to be used, now and in the future.   Conducting a forest inventory for a woodlot can have very different objectives compared due diligence on 20,000ha.  Narrowing these down early helps avoid problems in the statistical design of the inventory later on.

111314_0144_forestinven1Figure 1: Objectives

Obtain Prior Information

Forest/stand information can be useful when planning forest inventory. Previous inventories are a preferable source of information but parameters as age, stocking, thinning/pruning regime will enable pre stratification according to below. There more information available the more likelihood for a cost efficient and well-planned inventory, enabling establishment of less plots, hereby increasing client cost/benefit ratio. Another good investment before establishing ground plots is up-to-date mapping. Good accurate maps will not only save field crews time on the ground, but as well enable better sampling due to updated Net Stocked Area (NSA).
Update Net Stocked Area

Reliable and accurate data requires good planning and implementation. A recent NZIF article names one of the major pitfalls of volume inaccuracy as error in the calculation of NSA (Woollons, 2014). Since NSA changes over time (Figure 2), one of the initial steps in forest inventory planning should be forest NSA mapping. NSA is a horizontal area based on an aerial assessment, which must match actual measurements on the ground. Another key component is the removal of gaps hereby reducing variability.   Also ensure that field crews replicate these assumptions of NSA when installing plots on the ground and therefore are briefed on what boundary definitions are being used and have an upto date map.

111314_0144_forestinven2111314_0144_forestinven3Figure 2: Changes to NSA as canopy grows and shrinks after thinning (Pieres Maclaren, 2000) 

Design a Sampling Strategy and Sampling Population (Stratification)

Another cost efficient/cost beneficial method to decrease variety is stratification. Stratification is the segregation of a sampling frame (forest) into area known homogenous sub-populations (strata). Foresters typically stratify based on species, age, productivity, silviculture etc.

111314_0144_forestinven4

Figure 3: Forest stratified into individual strata’s

Sampling design and plots to establish varies depending on inventory type and objective, typical a sampling error of 5-10 percent difference in predicting volumes is accepted in inventories close to harvest, while 20 percent or more might be a reasonable sampling error for mid-rotation inventories. Mapping of plot locations are usually established by the use of Simple Random Sampling (SRS), Plots is located in a systematic pattern (e.g. grid) rather than at random. Grid start point is often randomly assigned to avoid bias.

111314_0144_forestinven5Figure 4: SRS approach

Another sampling technique is the Quasi-random approach which allows for more plots to be planned then established. We have been successfully using it for a couple years now for jobs where we want the flexibility to alter the sample size without the need / complexity of resampling or trying to get the perfect sample size prior to visiting the block. It is a simple but elegant solution, which has been validated as a statistically valid approach. It allows us to re-think/review our resource inventories while in process. Hereby we can allocate inventory resources while the inventory is being collected without biasing our sample. Quasi random plot design is a interesting solution to the challenges of resource inventory. By making our sample size flexible, we are making simple gains in what we do today in sample plot placement which can yield some efficiency in what we do as resource foresters.
The implementation of above key planning component will provide the forest owner with an accurate forest inventory while saving both costs and time, by limiting the amount of plots to establish which more than justifies the planning initial component.

Our team will be happy to assist you in every component of forest inventory planning. If you need further assistance, please contact us.

References
  • Woollons, R. 2014. Woollons, R. 2014. Don’t blame the tree volume equations. New Zealand Jurnal of Forestry, 50(3): 37-39
  • Pieres Maclaren, J. 2000. How much wood has your woodlot got? Forest Research Bulletine No. 217

Submit a Comment