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Soil moisture plays a key role in the ecological capability of arid and semi-arid woodland. Reducing soil moisture due to frequent droughts causes pest prevalence and disease outbreak and the consequence of forest dieback. On the other hand, soil moisture is strongly correlated with the amount of radiation received on the Earth's surface. The sun's radiation is traditionally described often by aspect and sometimes by toposhape. The use of the hillshade map for estimating solar radiation is possible through developing GIS. The present study aimed to compare the relationship and the ability of these indices to describe the phenomenon of arid and semi-arid woodland decline better and more accurately in a case study in the west of Iran. To this aim, the aspect and toposhape layers were generated in 5 and 12 classes, respectively. Then, the hillshade map in range of 0-255 was made during the peak of summer heat. The comparison of the dieback ratio in the three characteristic histograms showed that the shade index, unlike the other two indicators, had a significant effect on forest drought (R2=0.91 for linear equation and R2=0.94 for quadratic equation). The results indicated that the application of hillshade in describing and analysing ecological processes by relying on soil moisture such as woodland dieback is superior to the other two indicators. It is suggested that this index be used to obtain a risk model to predict woodlands dieback which are under the pressure of frequent droughts due to climate change or other mortal factors. [ABSTRACT FROM AUTHOR]

The Zagros forests in Western Iran are valuable ecosystems that have been seriously damaged by human interference (harvesting the wood and forest sub-products, converting the forests to the agricultural lands, and grazing) and natural events (drought events and fire). In this study, we generated accurate land cover (LC), and tree canopy cover percentage (TCC%) maps for the forests of Shirvan County, a part of Zagros forests in Western Iran using Sentinel-2, Google Earth, and field data for protective management. First, we assessed the accuracy of Google Earth data using 300 random field plots in 10 different land cover types. For land cover mapping, we evaluated the performance of four supervised classification algorithms (minimum distance (MD), Mahalanobis distance (MaD), neural network (NN), and support vector machine (SVM)). The accuracy of the land cover maps was assessed using a set of 150 stratified random plots in Google Earth. We mapped the forest canopy cover by using the normalized difference vegetation index (NDVI) map, and field plots. We calculated the Pearson correlation between the NDVI values and the TCC% (obtained from field plots). The linear regression between the NDVI values and the TCC% was used to obtain the predictive model of TCC% based on the NDVI. The results showed that Google Earth data yielded an overall accuracy of 94.4%. The SVM algorithm had the highest accuracy for the classification of Sentinel-2 data with an overall accuracy of 81.33% and a kappa index of 0.76. The results of the forest canopy cover analysis showed a Pearson correlation coefficient of 0.93 between the NDVI and TCC%, which is highly significant. The results also showed that the linear regression model is a good predictive model for TCC% estimation based on the NDVI (r2 = 0.864). The results can be used as a baseline for decision-makers to monitor land cover change in the region, whether produced by human activities or natural events and to establish measures for protective management of forests. [ABSTRACT FROM AUTHOR]