Urmia University Forest Research and Development 2476-3551 7 4 2022 02 20 Forecasting deforestation and forest recovery using Land Transformation Model ‎‎(LTM) in Iranian Zagros forests Forecasting deforestation and forest recovery using Land Transformation Model ‎‎(LTM) in Iranian Zagros forests 527 544 121098 10.30466/jfrd.2021.53873.1572 FA Hadi Beygiheidarlou Forestry department, Faculty of Natural Resources, Urmia, Iran. 0000-0002-7070-6178 Abbas Banj Shafiei Professor, Department of Forestry, Faculty of Natural Resources, Urmia University, Urmia, I.R. Iran 0000-0003-1832-4882 Mahdi Erfanian Associate Professor, Department of Rangelands and Watershed Management, Faculty of Natural Resources, Urmia University, Urmia, I.R. Iran Amin Tayyebi Geospatial Data Scientist, ESRI, Redland, CA 92373, United States Ahmad Alijanpour Associate Professor, Department of Forestry, Faculty of Natural Resources, Urmia University, Urmia, I. R. Iran 0000-0003-3389-930X Journal Article 2021 09 19 Land use changes and its patterns in spatial and temporal scales occur in a non-linear way. Therefore, to predict the potential and negative effects of these changes on forest ecosystem services in future, nonlinear tools such as Artificial Neural Networks (ANNs) are needed. In this study for forecasting deforestation and recovery of Sardasht forests for 10, 20 and 30 years later, Land Transformation Model (LTM) based on ANNs and GIS was used. For this purpose, three different scenarios including time periods of 1997-2007, 1997-2017 and 2007-2017 were used, and deforestation and forest recovery of Sardasht using 14 variables for 2027, 2037 and 2047 were predicted. Results showed that over 20-year studied time period (1997 to 2017) despite 2372.57 ha recovery of Sardasht forests, 10314.63 ha deforestation occurred. Deforestation and forest recovery modeling by all three scenarios with good Receiver Operating Characteristic curve (or ROC curve) (more than 0.8) for all scenarios, show a definite and increasing deforestation process in Sardasht over the next three decades, so based on the 1997-2007 scenario, it is anticipated that 22296.24 ha of forests in the region will be destroyed over the next 30 years. The results of this research can be used for proper conservation planning and increasing regulatory programs in areas with high degradation potential‎. Land use changes and its patterns in spatial and temporal scales occur in a non-linear way. Therefore, to predict the potential and negative effects of these changes on forest ecosystem services in future, nonlinear tools such as Artificial Neural Networks (ANNs) are needed. In this study for forecasting deforestation and recovery of Sardasht forests for 10, 20 and 30 years later, Land Transformation Model (LTM) based on ANNs and GIS was used. For this purpose, three different scenarios including time periods of 1997-2007, 1997-2017 and 2007-2017 were used, and deforestation and forest recovery of Sardasht using 14 variables for 2027, 2037 and 2047 were predicted. Results showed that over 20-year studied time period (1997 to 2017) despite 2372.57 ha recovery of Sardasht forests, 10314.63 ha deforestation occurred. Deforestation and forest recovery modeling by all three scenarios with good Receiver Operating Characteristic curve (or ROC curve) (more than 0.8) for all scenarios, show a definite and increasing deforestation process in Sardasht over the next three decades, so based on the 1997-2007 scenario, it is anticipated that 22296.24 ha of forests in the region will be destroyed over the next 30 years. The results of this research can be used for proper conservation planning and increasing regulatory programs in areas with high degradation potential‎. https://jfrd.urmia.ac.ir/article_121098_dd739b5c4a4a6a0bd40cb1b0da902c95.pdf