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 opti-mization 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 infor-mation derived from ALS surveys.Two mainstream cluster-ing 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 solv-able.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 optimiza-tion 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.

Adrián Pascual;Sándor F.Tóth

Forest Research Centre,School of Agriculture,University of Lisbon,Tapada da Ajuda,1349-017 Lisboa,Portugal;School of Forest Sciences,University of Eastern Finland,P.O.Box 111,80101 Joensuu,Finland;School of Environmental and Forest Sciences,University of Washington,Seattle,WA,USA

林业研究（英文版）

[1]Adrián Pascual;Sándor F.Tóth-.Using mixed integer programming and airborne laser scanning to generate forest management units)[J].林业研究（英文版）,2022(01):217-226

FMUs

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