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The cognition of cropping pattern is important for planning and resource management .Remote sensing as a science and technology of spatial information and geographic information system due to having the analytical facilities can play a key role in determining the distribution of crops and their lands under cultivation. In this research, in order to identify and separate the lands under cultivation of the dominant crops in Lenjanat of Isfahan province, the multi-temporal images of Landsat 8 satellite, OLI sensor were used in the dates of April 17, July 6, and August 23 in 2016. Using maximum likelihood classification and normalized difference vegetation index (NDVI) of the agriculture crops in different periods of growth and according to their cropping calendar, the map of the cropping pattern of the area was determined. To evaluate the accuracy of the results, the produced maps were examined with reference data. Kappa coefficient and overall accuracy were 0.88 and 90%, respectively, in maximum likelihood classification, and 0.90 and 93%, respectively, in NDVI. Furthermore, statistics presented by Agricultural Jihad Organization of Isfahan province in the 2015-2016 crop year was used for evaluation. The results showed that there were differences equal to 10.2%, 18.6% and 1.8%, in the area under cultivation of wheat and barley, rice, and potato and forage, respectively, in maximum likelihood classification, comparing with the statistics of Agriculture Jihad while the results of NDVI comparing with Jihad statistics showed the errors equal to 6.6 %, 6.5 % and 3.2%, respectively, that indicated the better performance of temporal vegetation indices in estimation of area under cultivation according to its phenology. Investigation of land use and cropping pattern of this area indicate a high centralization of agricultural lands with high water requirements and industries on the proximity of Zayanderud River which necessitates the spatial analysis of land use in this area.

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Examining the effects of climate change on the oak spatial distribution, as the main species of Zagros forests and its ecological and economic values is of significant importance. Here, we used species distribution models for simulating current climatic suitability of oak and its potential changes in 2050 and 2070. For this purpose, five regression-based and machine learning approaches, four climatic variables related to temperature and precipitation and two optimistic (RCP 2.6) and pessimistic (RCP 8.5)  greenhouse-gas scenarios were used. The results of measuring the accuracy of models by AUC indicated the good performance of all algorithms and Random Forest achieved the highest accuracy (AUC = 0.95) among other methods. The results showed that in both time periods and under both scenarios, changes will occur in oak spatial distribution and the most severe one would be a 42.9 percent loss in the oak climatic suitability in 2070 under pessimistic scenario (RCP 8.5).
 

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The aim of this study was to do attribution of climate change to the extreme snow fall of Mazandaran province in the northern part of Iran and southern shores of the Caspian Sea in the vicinity of Alborz mountains. The area is not prone to extreme snow fall or even snow fall then this phenomenon has had great damages to the infrastructures of the region. The study is performed on the time interval of 1987-2017in winter time (DJF). Corresponding calculation is done for two parallel worlds of counterfactual i.e. without external forcing and factual with external forcing in the context of stochastic climate modeling. This is done by the chaotic dynamical 3-D model Lorenz-63. Then the two worlds of study are defined on the basis of LM-63 and SLM-63 as deterministic and stochastic climate models. Fakher- Planck equation had the role of implementing time evolution of the PDF into the modal. The conditional probability and Bayesian framework is the preliminaries of the method of this study. The model is belonging to the space state models and Bayesian recursive estimation. These all is the basis of the EnKF as nonlinear filtering approach to the nonlinear dynamical model of this study. It is tried to bring down all the related situation associated with the issue on the basis of sound mathematical, epistemological and physical foundations. All the computations are done on the environment of GIS, Matlab, Mathematica and Maple. Then causal theory of pearl (2000) is used as the evidence of verification for the whole process. The final results showed that the extreme snow of Mazandaran province is attributable to the climate forcing defined for the study 0.8978 in its PN causation, 0.1942 in its PS causation and 0.4519 in its PNS causation.