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.

确定了伊朗Nowshahr的Kheyroud的Hyrcanian森林的有效干扰指数。根据生态系统参数（包括剖面类型，坡度和海拔），将研究区域划分为景观马赛克。共生纹理指数作为Landsat TM，ETM +和OLI图像的前五个波段上主要的木材采伐扰动制度的森林扰动因子而得出。使用十种趋势分析筛选这些指标，并通过人工神经网络将其用于对收获制度的干扰进行建模。结果表明，所选指标可用于区分具有不同干扰强度的区域，因此可用于通过健康距离方法进行健康评估的环境中。