Matti Maltamo

What does it actually mean to measure a sample plot in forest?

Maltamo M. (2023). What does it actually mean to measure a sample plot in forest? Silva Fennica vol. 56 no. 4 article id 23005.

Author Info
  • Maltamo, University of Eastern Finland, Faculty of Science, Forestry and Technology, Joensuu ORCID 0000-0002-9904-3371 E-mail

Received 24 January 2023 Accepted 26 January 2023 Published 26 January 2023

Views 2341

Available at | Download PDF

Creative Commons License CC BY-SA 4.0 full-model-article23005

Sample plot measurement is one of the basic operations in forest sciences. A plot is delineated and usually some forest level information is gathered. A certain minimum size in tree level measurements is often applied. Nowadays electronic devices including hypsometer and caliper are utilized when a human is measuring trees. Tally tree measurement usually include diameter at breast height (DBH), tree classifications (living or dead, tree storey, health, timber assortment) and registration of tree species. Tree height is also measured at least from chosen sample trees from which also other attributes, such as age, crown height and growth, are considered. There may be also many other measurements, but this is a basic set of information for forest resource calculation or field reference of remote sensing-based modelling of forest attributes. The purpose of a sample plot measurement may, of course, be other than determining growing stock, for example soil, ground vegetation and health to mention a few, but tree stock measurement is typically needed in those cases, too.

Recently, many remote sensing-based systems have been used in field measurements. These include, e.g., terrestrial and mobile laser scanning (TLS and MLS, respectively). Smartphone based applications, such as Trestima have also been widely applied. Even unmanned aerial vehicles has been experimented. TLS can provide highly detailed information on recognized tree trunks and crowns (Luoma 2022) – partly information, which has never been available earlier or at least these measurements have been very slow and expensive to carry out. TLS information has many different scientific uses. Correspondingly, MLS operated from a harvester will provide in the near future a completely new type of information in forest operations. Although these information sources provide detailed data, the interpretation result of these data should be considered as an accurate forest resource estimate, not a sample plot measurement. This is due to a couple of facts. In a remote sensing-based interpretation, only a proportion of trees is recognized. By applying remote sensing, it is impossible to find all trees in all type of forests. On the other hand, sample plot measurement requires measurement of all trees belonging to a stratum. Of course, some trees may be forgotten to be measured in the “traditional field work” but this refers to single cases. Another, even more serious bottleneck is tree species recognition. In Finland, remote sensing-based interpretation usually provides information on Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst.) and combined broadleaved species class. However, this information is not correct. In a sample plot measurement, species should be correctly registered. Especially in the National Forest Inventory type of measurements this refers to all species. If there is, for example, bird cherry (Prunus padus L.) in a plot, it must be registered or should be recognized. In an Austrian field data set, there were over 20 tree species (Gollob 2022). It is not possible to recognize all of them by TLS data, for example.

Despite these problems, I still see possibilities for remote sensing-based sample plot measurement. This is not my research topic, but my personal opinion is that personal laser scanning (PLS) could be a tool for that. PLS is an approach where the laser scanner is operated by a human. Sample plot measurement can be organized so that each tree in a plot is walked around (Gollob 2022). This removes the problem of tree recognition with the exception of some tree groups. During the scanning, the operator can register tree species correctly. Nowadays, there are already rather cheap tablet or smartphone-based applications of PLS. The accuracy of these devices in tree trunk dimension estimation may need to be still improved, but this an obvious step towards remote sensing-based sample plot measurement. One may still ask, what is the benefit of walking around all trees with tablet compared to traditional field work. At least the provided point clouds offer more versatile information on trees and close surroundings around them – not only the DBH.

Matti Maltamo


Gollob C (2022) Mensuration using modern sensor technology in forest inventory. Doctoral thesis. The University of Natural Resources and Life Science, Vienna, Austria.

Luoma V (2022) Measuring tree growth using terrestrial laser scanning. Dissertationes Forestales 329.

Click this link to register to Silva Fennica.
Log in
If you are a registered user, log in to save your selected articles for later access.
Contents alert
Sign up to receive alerts of new content

Your selected articles
Your search results
Maltamo M., (1997) Comparing basal area diameter distributions esti.. Silva Fennica vol. 31 no. 1 article id 5609
Uuttera J., Maltamo M. (1995) Impact of regeneration method on stand structure.. Silva Fennica vol. 29 no. 4 article id 5562
Korhonen K. T., Maltamo M. (1991) The evaluation of forest inventory designs using.. Silva Fennica vol. 25 no. 2 article id 5444
Kilkki P., Maltamo M. et al. (1989) Use of the Weibull function in estimating the ba.. Silva Fennica vol. 23 no. 4 article id 5392
Maltamo M., (2024) What we pay attention to when we are in the fore.. Silva Fennica vol. 58 no. 2 article id 24020
Maltamo M., (2023) What does it actually mean to measure a sample p.. Silva Fennica vol. 56 no. 4 article id 23005
Maltamo M., (2022) Silva Fennica has improved publishing services b.. Silva Fennica vol. 56 no. 2 article id 10763
Maltamo M., (2022) The persistently developing role of remote sensi.. Silva Fennica vol. 56 no. 1 article id 10711
Maltamo M., (2021) 100 years of national forest inventories Silva Fennica vol. 55 no. 4 article id 10643
Maltamo M., (2020) Re-searching the forests Silva Fennica vol. 54 no. 4 article id 10452
Maltamo M., (2020) Change of the Subject Editor in Silva Fennica Silva Fennica vol. 54 no. 1 article id 10333
Maltamo M., (2019) Silva Fennica in 2019 Silva Fennica vol. 53 no. 1 article id 10164
Jääskeläinen J., Korhonen L. et al. (2024) Individual tree inventory based on uncrewed aeri.. Silva Fennica vol. 58 no. 3 article id 23042
Hardenbol A. A., Kuzmin A. et al. (2021) Detection of aspen in conifer-dominated boreal f.. Silva Fennica vol. 55 no. 4 article id 10515
Kukkonen M., Kotivuori E. et al. (2021) Volumes by tree species can be predicted using p.. Silva Fennica vol. 55 no. 1 article id 10360
Karjalainen T., Packalen P. et al. (2019) Predicting factual sawlog volumes in Scots pine .. Silva Fennica vol. 53 no. 4 article id 10183
Korhonen L., Repola J. et al. (2019) Transferability and calibration of airborne lase.. Silva Fennica vol. 53 no. 3 article id 10179
Maltamo M., Hauglin M. et al. (2019) Estimating stand level stem diameter distributio.. Silva Fennica vol. 53 no. 3 article id 10075
Maltamo M., Karjalainen T. et al. (2018) Incorporating tree- and stand-level information .. Silva Fennica vol. 52 no. 3 article id 10006
Korhonen L., Pippuri I. et al. (2013) Detection of the need for seedling stand tending.. Silva Fennica vol. 47 no. 2 article id 952
Villikka M., Packalén P. et al. (2012) The suitability of leaf-off airborne laser scann.. Silva Fennica vol. 46 no. 1 article id 68
Korpela I., Ørka H. O. et al. (2010) Tree species classification using airborne LiDAR.. Silva Fennica vol. 44 no. 2 article id 156
Suvanto A., Maltamo M. (2010) Using mixed estimation for combining airborne la.. Silva Fennica vol. 44 no. 1 article id 164
Maltamo M., Peuhkurinen J. et al. (2009) Predicting tree attributes and quality character.. Silva Fennica vol. 43 no. 3 article id 203
Peuhkurinen J., Maltamo M. et al. (2008) Estimating species-specific diameter distributio.. Silva Fennica vol. 42 no. 4 article id 237
Korhonen L., Korhonen K. T. et al. (2007) Local models for forest canopy cover with beta r.. Silva Fennica vol. 41 no. 4 article id 275
Kangas A., Mehtätalo L. et al. (2007) Modelling percentile based basal area weighted d.. Silva Fennica vol. 41 no. 3 article id 282
Mehtätalo L., Maltamo M. et al. (2006) The use of quantile trees in the prediction of t.. Silva Fennica vol. 40 no. 3 article id 333
Hotanen J.-P., Maltamo M. et al. (2006) Canopy stratification in peatland forests in Fin.. Silva Fennica vol. 40 no. 1 article id 352
Kangas A., Maltamo M. (2002) Anticipating the variance of predicted stand vol.. Silva Fennica vol. 36 no. 4 article id 522
Sironen S., Kangas A. et al. (2001) Estimating individual tree growth with the k-nea.. Silva Fennica vol. 35 no. 4 article id 580
Maltamo M., Eerikäinen K. (2001) The Most Similar Neighbour reference in the yiel.. Silva Fennica vol. 35 no. 4 article id 579
Kangas A., Maltamo M. (2000) Performance of percentile based diameter distrib.. Silva Fennica vol. 34 no. 4 article id 620
Kangas A., Maltamo M. (2000) Percentile based basal area diameter distributio.. Silva Fennica vol. 34 no. 4 article id 619
Tahvanainen T., Kaartinen K. et al. (2007) Comparison of approaches to integrate energy woo.. Silva Fennica vol. 41 no. 1 article id 310