Effective disturbance indices for Hyrcanian forests in Kheyroud, Nowshahr, Iran were determined. The study area was divided into landscape mosaics based on ecosystem parameters including profile type, slope and elevation. Co-occurrence texture indices were derived as forest disturbance factors on the first five bands of Landsat TM, ETM+ and OLI images for the prevailing wood harvest disturbance regimes. These indices were screened using ten types of trend analyses and used for modeling disturbance of the harvesting regime through artificial neural networks. The results show that the selected indices can be useful in distinguishing areas with different disturbance intensities and as such, used in the context of health assessment through the health distance method. The accuracy of the health maps derived from the indices [increasing disturbance] led to give rise higher disturbance classification accuracy.
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Project funding: The work was funded partly by University of Zabol under Grant Number UOZ-GR-9616-145.
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Corresponding editor: Tao Xu.
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Salmanmahiny, A., Erfani, M., Danehkar, A. et al. Image texture indices and trend analysis for forest disturbance assessment under wood harvest regimes. J. For. Res. 32, 579–587 (2021). https://doi.org/10.1007/s11676-020-01117-7
- Ecosystem health
- Trend analysis
- Co-occurrence texture indices
- Landscape mosaics