The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-3/W8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W8, 301–305, 2019
https://doi.org/10.5194/isprs-archives-XLII-3-W8-301-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W8, 301–305, 2019
https://doi.org/10.5194/isprs-archives-XLII-3-W8-301-2019

  22 Aug 2019

22 Aug 2019

POST-FIRE FORESTRY RECOVERY MONITORING USING HIGH-RESOLUTION MULTISPECTRAL IMAGERY FROM UNMANNED AERIAL VEHICLES

L. Pádua1,2, T. Adão1,2, N. Guimarães1, A. Sousa1,2, E. Peres1,2, and J. J. Sousa1,2 L. Pádua et al.
  • 1Engineering Department, School of Science and Technology, University of Trás-os-Montes e Alto Douro, Vila Real, Portugal
  • 2Centre for Robotics in Industry and Intelligent Systems (CRIIS), INESC Technology and Science (INESC-TEC), Porto, Portugal

Keywords: Post-fire management, forest regeneration, fire severity, unmanned aerial vehicles, photogrammetric processing, multispectral imagery, digital image processing

Abstract. In recent years unmanned aerial vehicles (UAVs) have been used in several applications and research studies related to environmental monitoring. The works performed have demonstrated the suitability of UAVs to be employed in different scenarios, taking advantage of its capacity to acquire high-resolution data from different sensing payloads, in a timely and flexible manner. In forestry ecosystems, UAVs can be used with accuracies comparable with traditional methods to retrieve different forest properties, to monitor forest disturbances and to support disaster monitoring in fire and post-fire scenarios. In this study an area recently affected by a wildfire was surveyed using two UAVs to acquire multi-spectral data and RGB imagery at different resolutions. By analysing the surveyed area, it was possible to detect trees, that were able to survive to the fire. By comparing the ground-truth data and the measurements estimated from the UAV-imagery, it was found a positive correlation between burned height and a high correlation for tree height. The mean NDVI value was extracted used to create a three classes map. Higher NDVI values were mostly located in trees that survived that were not/barely affected by the fire. The results achieved by this study reiterate the effectiveness of UAVs to be used as a timely, efficient and cost-effective data acquisition tool, helping for forestry management planning and for monitoring forest rehabilitation in post-fire scenarios.