The use of airborne laser scanning (ALS) has enhanced forest inventory during the last decades due to the increasing capability of lasers to describe the three-dimensional structure of forests. This research focuses on the integration of ALS-based forest inventory into forest planning when the aim is to create dynamic treatment units (DTUs). In this approach, the management units are not fixed and predefined. They are temporary and formed by aggregating fine-grained inventory units. Management objectives and forest dynamics are the drivers of that aggregation process.
The research was conducted in two pine forests in Castilla y León (Spain) in which ground and ALS data were collected. This PhD thesis reviews four manuscripts concerning the implementation DTU (studies I and III), the implications of using alternative forest inventory units (FIU) in two types of problem formulations (studies I, III and IV), and the impact of plot positioning errors on the whole planning process starting from the sampling stage and ending with decision-making (study II). In all studies, growing stock attributes were estimated with ALS statistics, while diameter distributions and stand dynamics models developed in permanent plots were used to predict growing stock attributes. The alternative management schedules developed during the simulation phase aimed at maximizing a utility function composed of non-spatial (study III) and spatial objective variables (all studies).
The findings of this work highlight the good performance of irregular types of FIU and the benefit of using segmentation techniques when the aim is to generate compact DTUs. The use of spatial optimization improved the spatial layout of forest plans at a minor cost compared to non-spatial formulations. The use of spatial goals and spatially explicit harvest cost functions enhance the aggregation of FIUs. Heuristic-based optimization methods were effective when solving spatial combinatorial problems.
This PhD shows how the combination of ALS-based methods, widely used in forestry practice and spatial optimization contribute to the development of forest management planning methods.