Forest Stand Growth Forecasting in the Context of Changes in the Insolation of Building Roofs

Abstract

This article analyzed the long-term impact of tree growth on the decrease in sunlight of a planned photovoltaic installation. As trees grow, they can obstruct sunlight and reduce the amount of insolation reaching the PV panels, and knowledge about the degree of this reduction is crucial when assessing the long-term economic effects of the investment. Currently, when planning PV installation, the roof facing, latitude, topography, and neighboring buildings are taken into account. However, there is no tool to assess the impact of tree growth over time on changes in the level of sunlight on the roof surface. The authors propose a tool for performing such an assessment using geospatial analysis techniques. The data from airborne laser scanning (ALS) and unmanned aerial vehicles with laser scanning (ULS) were used to model trees in two epochs. The authors used two epochs of data to evaluate mathematical models of tree growth. The evaluated tree growth model was then used to predict forest stand growth over a 30-year period and to assess the change in sunlight due to the modeled growth. For the case study, two test sites have been taken into account. At site 1 and site 2, respectively, 25 and 12 points representing the centers of potential photovoltaic panels were designed, for which the annual sum of minutes during which the point remained exposed to sunlight was calculated. The results showed that the use of ALS and ULS provided valuable data for determining current and predicted shading of trees. Moreover, the presented studies showed that the changes in forest stand growth had a significant impact on decreasing the insolation of building construction. In the case of both test sites, the change in tree height after 30 years resulted in a reduction in the number of minutes of sunlight by more than 50%. The authors suggest that the developed technique should be incorporated into PV installation planning tools to ensure reliable prediction of the long-term profitability of designed PV installations.