The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1083–1088, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1083-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1083–1088, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1083-2019

  05 Jun 2019

05 Jun 2019

AUTOMATIC DETECTION OF FOREST-ROAD DISTANCES TO IMPROVE CLEARING OPERATIONS IN ROAD MANAGEMENT

A. Novo1, H. González-Jorge2, J. Martínez-Sánchez1, L. M. González-de Santos1, and H. Lorenzo3 A. Novo et al.
  • 1Geotech Group, Department of Natural Resources and Environmental Engineering, School of Mining Engineering, University of Vigo, 36310, Vigo, Spain
  • 2Geotech Group, Department of Natural Resources and Environmental Engineering, School of Aerospace Engineering, University of Vigo, 32004, Ourense, Spain
  • 3Geotech Group, Department of Natural Resources and Environmental Engineering, School of Forestry Engineering, University of Vigo, 36005, Pontevedra, Spain

Keywords: Mobile LiDAR, Road Safety Distance, Forestry Management, Forest Fire, Point Cloud Processing

Abstract. There is a complex relation between roads and fires. Several major wildfires were ignited near to roads (Morrison 2007) and how they progressed is an important role to understand the importance to forest management in this environment. Nowadays, a sustainable forest management is necessary both for environment and politics. One of the reasons of road management is that these infrastructures provide an effective firewall in case of forest fires and an escape route for the population. Forest management optimization in road surroundings would improve wildfires prevention and mitigate their effects. One of the main indicators of road safety is the distance between road and vegetation.

The aim of this work is to develop a methodology to determine what areas do not obey current laws about safety distances between forest and roads. The acquisition of LiDAR data is done by Lynx Mobile Mapper System from University of Vigo. The methodology is automated using LiDAR data processing. The developed algorithms are based in height and length segmentation of the road. The objective is classifying vegetation groups by height and calculate the distance to the edges of road. The vegetation is divided in groups of height of 5, 10, 15 and 30 m. The minimum distance calculation is 2 m, for the vegetation of 5 m height and a maximum of 60 m for vegetation 30 m height. The height of vegetation has a directly relation with the distance separation with the road.