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

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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.

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Abbreviations

ALS:

Airborne laser scanning

3D:

Three dimensional

CHM:

Canopy height model

DTM:

Digital terrain model

FMUs:

Forest management units

GEOBIA:

Geospatial object- based image analysis

GIS :

Geographic information systems

LiDAR:

Light detection and ranging

OF:

Objective function

MIP:

Mixed integer programming

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Acknowledgements

The authors thank the University of Eastern Finland and the University of Washington for financial, technical, and scientific support during the first stages of the research. The study also benefited from the research exchange platform provided by the SuFoRun project (Marie Sklodowska-Curie Grant Agreement No. 691149). The first author (I.P. in the scope of Norma Transitória –DL57/2016/CP5151903067/CT4151900586) was supported by Fundação para a Ciência e a Tecnologia through the MODFIRE project—A multiple criteria approach to integrate wildfire behavior in forest management planning (PCIF/MOS/0217/2017).

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Authors and Affiliations

  1. Forest Research Centre, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017, Lisboa, Portugal

    Adrián Pascual

  2. School of Forest Sciences, University of Eastern Finland, P.O. Box 111, 80101, Joensuu, Finland

    Adrián Pascual

  3. School of Environmental and Forest Sciences, University of Washington, Seattle, WA, USA

    Sándor F. Tóth

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  1. Adrián Pascual
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  2. Sándor F. Tóth
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Correspondence to Adrián Pascual.

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Project funding: This study was supported by MODFIRE project—A multiple criteria approach to integrate wildfire behavior in forest management planning (PCIF/MOS/0217/2017), and benefited from the research exchange platform provided by the SuFoRun project (Marie Sklodowska-Curie Grant Agreement No. 691149).

The online version is available at http://www.springerlink.com.

Corresponding editor: Yu Lei.

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Pascual, A., Tóth, S.F. Using mixed integer programming and airborne laser scanning to generate forest management units. J. For. Res. 33, 217–226 (2022). https://doi.org/10.1007/s11676-021-01348-2

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