We use some essential cookies to make this website work.
We’d like to set additional cookies to understand how you use forestresearch.gov.uk, remember your settings and improve our services.
We also use cookies set by other sites to help us deliver content from their services.
Preparing to search
From 1987 until 2006 the Forestry Commission monitored annual changes in the condition of Britain’s forest trees by assessing the status of five forest species via a network of permanent monitoring plots distributed throughout the country. The species assessed were: Sitka spruce (Picea sitchensis), Norway spruce (P. abies), Scots pine (Pinus sylvestris), oak (Quercus spp.) and beech (Fagus sylvatica). The results (by year and by species) for the period 2000–2006 are presented here.
Methodology
The plots consisted of 24 trees located in four sub-plots of six trees and, depending upon the species assessed, between 29 and 33 features indicative of condition were scored for each tree. Evaluations of the incidence of flowering and fruiting, or the incidence of damage by insects or fungi were made, but the feature of greatest interest was an assessment of crown density. This is an estimate of the degree of opacity of the crown, which is used as an index of tree condition. Until 1993, the basis for comparison used in the surveys conducted in the United Kingdom was an ‘ideal’ tree carrying the maximum possible amount of foliage. However, in similar surveys conducted in most other European countries, comparisons are most commonly made with reference to a tree with full foliage under local conditions (the ‘local tree’ method). Usually this method involves selecting the tree with the greatest amount of foliage in the general vicinity of a survey plot to serve as a standard against which the plot trees are assessed. The same local tree is generally retained from year to year but it may be replaced by another tree in the event that its condition deteriorates. In order to harmonise with results obtained in other countries, crown density estimates in the United Kingdom have been made using the local tree method since 1993. However, to maintain the existing time series of crown density figures, all plot trees have also been assessed using the previous idealised standard.
Reductions in crown density are estimated in 5% classes by reference either to a standard set of photographs of ‘ideal’ trees or to ‘instant’ photographs of individual local reference trees. Data are collected on hand-held computers and are checked for consistency and for departures from expected values both in the field and before analysis. Except where otherwise stated, the crown density results presented here are those obtained by comparison with an ‘ideal’ standard.
To check the consistency of the crown density scores made by teams of assessors involved in the survey, a proportion of the plots are re-assessed by experienced supervisors. The assessments of the supervisors are also checked against each other in a separate exercise.
References and useful sources of information
References and supporting publications
Last updated: 13th March 2018
Cookies are files saved on your phone, tablet or computer when you visit a website.
We use cookies to store information about how you use the dwi.gov.uk website, such as the pages you visit.
Find out more about cookies on forestresearch.gov.uk
We use 3 types of cookie. You can choose which cookies you're happy for us to use.
These essential cookies do things like remember your progress through a form. They always need to be on.
We use Google Analytics to measure how you use the website so we can improve it based on user needs. Google Analytics sets cookies that store anonymised information about: how you got to the site the pages you visit on forestresearch.gov.uk and how long you spend on each page what you click on while you're visiting the site
Some forestresearch.gov.uk pages may contain content from other sites, like YouTube or Flickr, which may set their own cookies. These sites are sometimes called ‘third party’ services. This tells us how many people are seeing the content and whether it’s useful.