Canopy gap delineation using UAV data in a Hyrcanian forest (Case study: Shastklateh Forest)

Document Type : Research Paper

Authors

1 Ph.D. Candidate of Forestry, Dept. of Forestry, Faculty of Forest Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, I. R. Iran

2 Prof., Dept. of Forestry, Faculty of Forest Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, I. R. Iran

3 Associate Prof., Dept. of Forestry, Faculty of Forest Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, I. R. Iran.

4 4Associate Prof., Dept. of Silviculture and Forest Ecology, Faculty of Forest Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, I. R. Iran

Abstract

Canopy gap delineation is essential for achieving a better comprehension of forest structure. This study aims to (a) extract canopy gaps using UAV data and (b) compare the performance of different canopy gap extraction methods in a managed stand in the northeast of Iran. A canopy height model (CHM) was produced by subtracting LIDAR digital terrain model from the UAV digital surface model. CHM classification performs to extract gaps by thresholding CHM (fixed height and CHM slope and relative height thresholds) and object-based classification on the UAV CHM and orthophoto. Ground truth is produced in the point and polygon forms through field measurements and visual interpretation of the UAV orthophoto. The geometry of the canopy gaps (Area, perimeter, and shape complexity) was calculated. Finally, the point and polygon base accuracy of delineated gaps assess for each of the methods. Point accuracy assessment suggests that 60% CHM slope produces the highest overall accuracy and Kappa coefficient of 91.7% and 0.874, respectively. About area accuracy assessment, the best match between delineated gaps and ground truth polygons was achieved by using relative height and 60% CHM slope thresholds. The lowest mean errors of GSCI produced by 70% CHM slope (0.15). Moreover, object-based classification showed the lowest mean error of area (33.76 m2) and perimeter (16.80 m). In conclusion, while area accuracy is considered the best fit of the delineated gap's geometry is gained by the object-based classification.

Keywords

References

 
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Iranian Journal of Forest
Volume 14, Issue 2
September 2022
Pages 135-154

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History

  • Receive Date: 31 August 2021
  • Revise Date: 11 November 2021
  • Accept Date: 13 December 2021

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