The methods used to provide an accurate estimate of current and projected timber availability from private forests in Ireland have their limitations.

Project Output

The LiFOR project has  developed a framework that uses aerial laser scanning to improve forest productivity estimates both in terms of growth rates and productive forest area.

The project has developed a new procedure to convert LiDAR acquired tree canopy height estimates into the Top Height parameter commonly used in Irish forestry for estimating forest productivity. The project has produced a more accurate method for estimating private forest productivity and timber forecasting up to 2035.

With this information, the timber processors and other end users of forest products can better plan for the utilisation of the increasing private timber supply over the period 2016 – 2035. The project is also building capacity in Ireland in this fast-developing area of remote sensing.

Find out more about LiDAR here

Improving the forecast of forest productivity in Ireland using LiDAR data

One of the challenges for the forest industry in Ireland is to know what amount of timber will be produced by the private forest estate in the years ahead so that it can plan its products and services.

A timber supply forecast is compiled for Ireland on a five-year basis, providing an indication of the potential roundwood production over the following 20 years. However, there are a number of weaknesses in the current forecasting method, these range from using a standard estimate of non-planted areas in forests, tree species coverage in the model, and assumptions of growth rates of different forest tree species.

This study investigates the scope for using aerial LiDAR data to improve the forecasting of roundwood production in private forests in Ireland by integrating data derived from aerial LiDAR scanning into the existing methods. We used LiDAR data to estimate Top Height and, associated with this, the stand productivity, and to automatically delineate forest boundaries based on vegetation height differences. The LiDAR based estimates were compared to ground-truth data and to the results of the currently used methodologies. We set out to demonstrate that our new method is more accurate and more cost effective than the existing method.

The stand extent and productivity estimates obtained using the three methods were compared and then used as input in the harvest forecast software to determine the impact of their differences on the predicted roundwood volumes.

Main Output

Results from this study have indicated that the stand productivity estimates obtained using the LiDAR methodology are much closer to the field-based estimates than those obtained using the current method that is based on bio-physical site factors that can be extracted from existing GIS-based data bases. The relationships between the LiDAR estimates and the field-based ones are strongly linear and allow for the easy adjustment of the Top Height estimates from LiDAR data to the field-based ground truth values. In terms of productive area estimates, the results indicate that the uniform adjustment factor of 15% is too high overall. The combined effects of these two new estimation methods for area and productivity on the harvest forecast is still being analysed at this moment. However, the study has produced new pathways to improve forecast accuracy in terms of productivity and productive area, leading to better informed business and policy decisions in terms of timber production, carbon accounting and non-timber goods and services.

Project Objectives

Explore how aerial LiDAR can improve productivity estimation of the private sector forest resource for forecasting purposes, based on the current Forest Service forest inventory GIS layer;

Develop mechanism(s) to convert LiDAR acquired canopy height estimates into the Top Height parameter commonly used in Irish forestry from: 1) A canopy height to Top Height model; 2) Top Height determined directly from raw LiDAR;

Establish field survey plots to validate the remotely sensed data. Investigate using NFI plots that fall within test area for ground truthing;

Compare the current approach to productivity estimation with the new productivity estimation approaches from current inventory;

Conduct a like-for-like analysis for the test area by comparing forecast outputs under the current method and method(s) that use remote sensing to update forecast inputs, from private forecast model;

Explore the suitability of the derived information for the test area to be disseminated via a public web portal


Ireland’s 5-year timber supply forecast provides an indication of the potential roundwood production over the following 20 years. This is valuable information to the wood processing industry, timber growers and policy makers who help maximise the production benefits from forestry in a sustainable way. Compiling an accurate picture of timber availability is challenging, and our knowledge of the current state of private forests and how fast they are growing is currently limited for this purpose.

LiFOR has explored an innovative framework toward identifying productivity estimates for Irelands’ private forests. The use of aerial LiDAR to estimate forest productivity both in terms of growth rates and productive forest area is explored. This was facilitated through combining LiDAR data with manually recorded forest inventory plots to create productivity estimates for a range of Irish forest species. These estimates were then used for a comparison with the private forecast.


These are the people that were involved in the LIFOR project

Project Leader: Prof Maarten Nieuwenhuis

Master student: Ciaran Walsh

Dissemination: Charles Harper