Assessment of the effect of stand density on the height growth of Scots pine using repeated ALS data

Abstract

• Repeated ALS allows detecting the height increment variation influenced by stand density. • Higher stand density stimulate height growth, particularly on most productive sites. • Repeated ALS data allows developing density-dependent top height growth models. The spread of remote sensing technologies allows for continuous and precise mapping of forest ecosystem attributes over large areas. One of the most important attributes of a forest ecosystem is site productivity, an essential determinant of resource availability. Knowledge of site productivity is crucial for sustainable forest management because it is the basis for predictions of future forest growth and is fundamental to long-term planning in forestry. Models of forest dynamics allow us to better understand the functioning of forest ecosystems and to assess the provision of ecosystem services. However, discussions on the factors affecting forest growth are ongoing. Previous research has not clarified the influence of stand density on the height increment of trees. Consequently, this uncertainty leads to inconsistent predictions for stand growth and nonoptimal forest-management decisions. We aimed to analyse the effect of stand density on top height (TH) growth and the development of the TH growth model for Scots pine based on bitemporal ALS measurements. We applied repeated ALS observations from two acquisitions (2011, 2019) to fit a density-sensitive TH growth model. We found that stand density affected the TH growth of Scots pine. A higher stand density stimulated TH growth, particularly in productive sites showing a TH increment of about 10%. We demonstrated the usefulness of bitemporal ALS measurements for developing a TH growth model for Scots pine considering stand density. We found that the predictive accuracy of the TH increment increased when stand density was considered.