Detection of Oak Stands Dieback Using Remote Sensing (Case study: Some Parts of Lorestan Province Forests)

Document Type : Research Paper

Authors

Department of Forestry, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran

Abstract

The crisis of decline or drying of oak trees in the Zagros forests is one of the problems we have faced in recent years. The first step in managing this crisis is to map the affected areas and classify the severity of deforestation. The present study aimed to evaluate the Landsat OLI capability to map oak stands dieback in the Koohdasht city of Lorestan province. In addition to the main bands and fused bands with the 15-meter panchromatic band of the OLI sensor, suitable vegetation indices and the first components from PCA were also applied in the claasification. 150 square sample plots with dimensions of 30 × 30 meters were recorded to produce ground truth map. Data classification was done using minimum distance to mean, maximum likelihood and artificial neural networks classifiers in 5 classes of dieback and accuracy assessment was done using ground truth map. Because of the low separability of some classes, these classes were merged. Finally, classification with three and two classes (healthy, dieback) was performed. The highest overall accuracy of 53%, 75% and 91% and Kappa coefficient of 0.43, 0.64 and 0.71 was obtained using fused bands and artificial neural networks classifier for five, three and two dieback classes, respectively. The results demonstrated high performance of Landsat 8-OLI for mapping of healthy and oak dieback areas, but low to moderate functionality for identification of the intensity of oak decline in the study area.

Keywords

References

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History

  • Receive Date: 21 October 2020
  • Revise Date: 18 January 2021
  • Accept Date: 02 February 2021

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