Large scale semi-automatic detection of forest roads from low density LiDAR data on steep terrain in Northern Spain

doi:10.3832/ifor2989-012

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Large scale semi-automatic detection of forest roads from low density LiDAR data on steep terrain in Northern Spain

Covadonga Prendes⁽¹⁾ , Sandra Buján⁽²⁾, Celestino Ordoñez⁽³⁾, Elena Canga⁽¹⁾

iForest - Biogeosciences and Forestry, Volume 12, Issue 4, Pages 366-374 (2019)
doi: https://doi.org/10.3832/ifor2989-012
Published: Jul 05, 2019 - Copyright © 2019 SISEF

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While forest roads are important to forest managers in terms of facilitating the exploitation of wood and timber, their role is far more multifunctional. They permit access to emergency services in the case of forest fires as well as acting as fire breaks, enhance biodiversity, and provide access to the public to enjoy recreational activities. Detailed maps of forest roads are an essential tool for better and more timely forest management and automatic/semi-automatic tools allow not only the creation of forest road databases, but also enable these to be updated. In Spain, LiDAR data for the entire national territory is freely available, and the capture of higher density data is planned in the next few years. As such, the development of a forest road detection methodology based on LiDAR data would allow maps of all forest roads to be developed and regularly updated. The general objective of this work was to establish a low density LiDAR data-based methodology for the semi-automatic detection of the centerline of forest roads on steep terrain with various types of canopy cover. Intensity and slope images were generated using the currently available LiDAR data of the study area (0.5 points m^-2). Two image classification approaches were evaluated: pixel-based and object-oriented classification (OBIA). The LiDAR-derived centerlines obtained with the two approaches were compared with the real centerlines which had previously been digitized in the field. The road width, type of surface and type of vegetation cover were also recorded. The effectiveness of the two approaches was evaluated through three quality indicators: correctness, completeness and quality. In addition, the accuracy of the LiDAR-derived centerlines was also evaluated by combining GIS analysis and statistical methods. The pixel-based approach obtained higher values than OBIA for two of the three quality measures (correctness: 93% compared to 90%; and quality: 60% compared to 56%) as well as in terms of positional accuracy (± 5.5 m vs. ± 6.8 for OBIA). The results obtained in this study demonstrate that producing road maps is among the most valuable and easily attainable products of LiDAR data analysis.

Keywords

GIS, Pixel-based Classification, OBIA, Quality Measures, Forest Roads Network, Accuracy Assessment

Authors’ address

(1)

0000-0003-2132-9991
CETEMAS, Centro Tecnológico y Forestal de la Madera, Área de Desarrollo Forestal Sostenible. Pumarabule, Carbayín Bajo s/n 33936 Siero (Spain)

(2)

0000-0003-1956-0078
Laboratorio do Territorio (LaboraTe), Universidad de Santiago de Compostela. C/ Benigno Ledo Campus Universitario 27002 Lugo (Spain)

(3)

Departamento de Explotación de Minas, Grupo de Investigación en Geomática y Computación Gráfica (GEOGRAPH), Universidad de Oviedo, 33004 Oviedo (Spain)

Corresponding author

Covadonga Prendes
cprendes@cetemas.es

Citation

Prendes C, Buján S, Ordoñez C, Canga E (2019). Large scale semi-automatic detection of forest roads from low density LiDAR data on steep terrain in Northern Spain. iForest 12: 366-374. - doi: 10.3832/ifor2989-012

Academic Editor

Agostino Ferrara

Paper history

Received: Nov 05, 2018
Accepted: Apr 15, 2019

First online: Jul 05, 2019
Publication Date: Aug 31, 2019
Publication Time: 2.70 months

Open Access

This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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