Using mixed integer programming and airborne laser scanning to generate forest management units

Abstract

Airborne laser scanning (ALS) has been widely applied to estimate tree and forest attributes, but it can also drive the segmentation of forest areas. Clustering algorithms are the dominant technique in segmentation but spatial optimization using exact methods remains untested. This study presents a novel approach to segmentation based on mixed integer programming to create forest management units (FMUs). This investigation focuses on using raster information derived from ALS surveys. Two mainstream clustering algorithms were compared to the new MIP formula that simultaneously accounts for area and adjacency restrictions, FMUs size and homogeneity in terms of vegetation height. The optimal problem solution was found when using less than 150 cells, showing the problem formulation is solvable. The results for MIP were better than for the clustering algorithms; FMUs were more compact based on the intra-variation of canopy height and the variability in size was lower. The MIP model allows the user to strictly control the size of FMUs, which is not possible in heuristic optimization and in the clustering algorithms tested. The definition of forest management units based on remote sensing data is an important operation and our study pioneers the use of MIP ALS-based optimal segmentation.