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Title:
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Using spatial optimization to create dynamic harvest blocks from LiDAR-based small interpretation units
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Author:
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Pascual, Adrián; Pukkala, Timo; Rodríguez Puerta, Francisco; Miguel Magaña, Sergio de
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Notes:
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Spatial and temporal differences in forest features occur on different scales as forest
ecosystems evolve. Due to the increased capacity of remote sensing methods to detect these differences,
forest planning may now consider forest compartments as transient units which may change in time
and depend on the management objectives. This study presents a methodology for implementing
these transient units, referred to as dynamic treatment units (DTU). LiDAR (Light Detecting and
Ranging) data and field sample plots were used to estimate forest stand characteristics for 500-m2
pixels and compartments, and a set of models was developed to enable growth simulations. The DTUs
were obtained by maximizing a utility function which aimed at maximizing the aggregation of
harvest areas and the ending growing stock volume with even-flow cutting targets for three 10-year
periods. Remote sensing techniques, modeling, simulation, and spatial optimization were combined
with the aim of having an efficient methodology for assigning cutting treatments to forest stands
and delineating compact harvest blocks. Pixel-based planning led to more accurate estimation
of stand characteristics and more homogeneity inside the delineated harvest blocks while the
compartment-based planning resulted in larger and higher area/perimeter ratio.
Financial support to conduct this study was obtained from Cost Action FP1206 “EuMixFor” (through a Short Term Scientific Mission titled “Optimization applied to forest management in mixed European forests”) as well as from the 2014 Mediterranean Model Forests grant awarded by EFIMED (Mediterranean Regional Office of the European Forest Institute) and MMFN (Mediterranean Model Forest Network). |
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Subject(s):
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-Forest planning
-Spatial optimization
-Precision forestry
-Remote sensing |
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Rights:
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cc-by (c) Pascual, Adrián. et al., 2016
http://creativecommons.org/licenses/by/3.0/es/
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Document type:
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article
publishedVersion |
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Published by:
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MDPI
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Share:
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