The distribution of calcareous nannofossils in the late Santonian–Late Maastrichtian deposits in the southwest of Iran (Khuzestan Province)

The first data on the distribution of calcareous nannofossils in the Behbehan section, the Kuh-e-Rish, are considered. According to the distribution of nannofossils, the Upper Cretaceous deposits of the section are subdivided into nine biostratigraphic zones. CC17 (Calculites obscurus zone) indicate the Late Santonian. Biozones CC18 (Aspidolithus parcus zone), CC19 (Calculites ovalis zone), CC20 (Ceratolithoides aculeus zone), CC21 (Quadrum sissinghii zone), and CC22 (Quadrum trifidum zone) represent the Campanian. Biozone CC23 (Tranolithus phacelosus zone) indicate the Late Campanian–Early Maastrichtian. Biozones CC24 (Reinhardtites levis zone) and CC25 (Arkhangelskiella cymbiformis zone) suggest the Middle and Late Maastrichtian, respectively. In the late Late Maastrichtian, due to decreasing in water depth at the study area, Nephrolithus frequens zone (CC26) defined in Tethysian domain was not recognized. The boundary between Gurpi–Pabdeh Formations represented a non-depositional period from the late Late Maastrichtian to the end of Early Paleocene. Also, it seems that predominant conditions of the sedimentary environment of Neotethys basin with the presence of index species calcareous nannofossils specified, which itself indicates that the warm climate and high depth of the basin in Late Santonian to Late Maastrichtian, in low latitudes has been prevalent.     

A new approach for forest fire risk modeling using fuzzy AHP and GIS in Hyrcanian forests of Iran

The presented research was performed in order to model the fire risk in a part of Hyrcanian forests of Iran. The fuzzy sets integrated with analytic hierarchy process (AHP) in a decision-making algorithm using geographic information system (GIS) was used to model the fire risk in the study area. The used factors included four major criteria (topographic, biologic, climatic, and human factors) and their 17 sub-criteria. Fuzzy AHP method was used for estimating the importance (weight) of the effective factors in forest fire. Based on this modeling method, the expert ideas were used to express the relative importance and priority of the major criteria and sub-criteria in forest fire risk in the study area. The expert ideas mean was analyzed based on fuzzy extent analysis. Then, the fuzzy weights of criteria and sub-criteria were obtained. The major criteria models and fire risk model were presented based on these fuzzy weights. On the other hand, the spatial data of 17 sub-criteria were provided and organized in GIS to obtain the sub-criteria maps. Each sub-criterion map was converted to raster format and it was reclassified based on risk of its classes to fire occurrence. Then, all sub-criteria maps were converted to fuzzy format using fuzzy membership function in GIS. The fuzzy map of each major criterion (topographic, biologic, climatic, and human criteria) was obtained by weighted overlay of its sub-criteria fuzzy maps considering to major criterion model in GIS. Finally, the fuzzy map of fire risk was obtained by weighted overlay of major criteria fuzzy maps considering to fire risk model in GIS. The actual fire map was used for validation of fire risk model and map. The results showed that the fuzzy estimated weights of human, biologic, climatic, and topographic criteria in fire risk were 0.301, 0.2595, 0.2315, and 0.208, respectively. The results obtained from the fire risk map showed that 38.74% of the study area has very high and high risk for fire occurrence. Results of validation of the fire risk map showed that 80% of the actual fires were located in the very high and high risk areas in fire risk map. It can show the acceptable accuracy of the fire risk model and map obtained from fuzzy AHP in this study. The obtained fire risk map can be used as a decision support system for predicting of the future fires in the study area.