ISPRS-Annals ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS-Annals ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. 2194-9050 Copernicus Publications Göttingen, Germany 10.5194/isprs-annals-IV-3-W1-33-2019 PLANNING HARVESTING OPERATIONS IN FOREST ENVIRONMENT: REMOTE SENSING FOR DECISION SUPPORT Piragnolo M. 1 2 Grigolato S. 1 Pirotti F. 1 2 https://orcid.org/0000-0002-4796-6406 TESAF Department , University of Padova, Via dell’Università, 16, 35020 Legnaro (PD), Italy CIRGEO – Interdepartmental Research Center in Geomatics - University of Padova, Via dell’Università, 16, 35020 Legnaro (PD), Italy 01 03 2019 IV-3/W1 33 40 Copyright: © 2019 M. Piragnolo et al. 2019 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://isprs-annals.copernicus.org/articles/isprs-annals-IV-3-W1-33-2019.html The full text article is available as a PDF file from https://isprs-annals.copernicus.org/articles/isprs-annals-IV-3-W1-33-2019.pdf

The goal of this work is to assess a method for supporting decisions regarding identification of most suitable areas for two types of harvesting approaches in forestry: skyline vs. forwarder. The innovative aspect consists in simulating the choices done during the planning in forestry operations. To do so, remote sensing data from an aerial laser scanner were used to create a digital terrain model (DTM) of ground surface under vegetation cover. Features extracted from the DTM are used as input for several machine learning predictors. Features are slope, distance from nearest roadside, relative height from nearest roadside and roughness index. Training and validation is done using areas defined by experts in the study area. Results show a K value of almost 0.92 for the classifier with best results, random forest. Sensibility of each feature is assessed, showing that both distance and height difference from nearest road-side are more significant than overall DTM value.