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Rice cultivated areas and yield information is indispensable for sustainable management and economic policy making for this strategic food crop. Introduction of high spectral and special resolution satellite data has enabled production of such information in a timely and accurate manner. Knowledge of the spectral reflectance of various land covers is a prerequisite for their identification and study. Evaluation of the spectral reflectance of plants using field spectroradiometry provides the possibility to identify and map different rice varieties especially while using hyperspectral remote sensing. This paper reports the results of the first attempt to evaluate spectral signatures of seven north Iranian rice varieties (Fajr, Hybrid, Khazar, Nemat, Neda, Shiroudi and Tarom plots) in the experimental station of the Iranian Rice Research Institute (main station in Amol, Mazanderan Province). Measurements were carried out using a field spectroradiometer in the range of 350-2,500 nm under natural light and environmental conditions. In order to eliminate erroneous data and also experimental errors in spectral reflectance curves, all curves were individually quality controlled. A set of important vegetation indices sensitive to canopy chlorophyll content, photosynthesis intensity, nitrogen and water content were employed to enhance probable differences in spectral reflectance among various rice varieties. Analysis of variance and Tukey’s paired test were then used to compare rice varieties. Using Datt and PRI1 indices, significant differences (= 0.01) were found among rice varieties reflectances in 19 out of 21 cases. This promises the possibility of accurate mapping of rice varieties cultivated areas based on hyperspectral remotely sensed data.

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 While the hydrological balance of forest ecosystems has often been studied, quantitative studies on the seasonal variability of rainfall Interception (I) and Canopy Storage Capacity (S) by individual trees are less frequently reported. Hence, the effects of the seasonal variation in I and S by individual Persian oak trees (Quercus brantii var. Persica) in the Zagros forests of Iran were studied over a 1-year period. Annually, I accounted for 84.9 mm (20%) of Gross Rainfall (GR) that significantly differed between the in leaf (47.4 mm or 30% of GR) vs. leafless (37.7 mm or 14% of GR) periods. Negative logarithmic correlations existed between I:GR and GR both for in leaf (r²= 0.808) and leafless (r²= 0.709) periods.An indirect method, outlined by Pereira et al. (2009), estimated S to be 1.56 mm in the in Leaf Period (LP) and decreased considerably to 0.56 mm in the Leafless Period (LLP). The results indicate that while I decreased during the LLP, it still exerts considerable influence on the hydrology of forests. Hence, measurement of I in both the LP and LLP is essential when assessing the water balance on the catchment scale.

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Lack of fire behavior studies and the immediate needs posed by the extent of the fire problem in forests of Iran require that extensive studies be conducted to develop models to predict fire behavior in the region.In this study, FARSITE Fire Area Simulator was applied to simulate spread and behavior of two real fires that had occurred in Northern Forests of Iran during 2010 summer and fall seasons in a spatially and temporally explicit manner taking into account the fuel, topography, and prevailing weather in the area. Spatial data themes of elevation, aspect, slope, canopy cover, and fuel model were prepared and formatted in GIS along with weather and wind files to run FARSITE fire behavior model. The effect of weather conditions on the accuracy of FARSITE simulations was evaluated in order to assess the capabilities of the simulator in accurately predicting the fire spread in the case study. The WindNinja model was used to derive local winds influenced by vegetation and topography. The simulations were validated with the real mapped fire scars by GPS mapping. Kappa Coefficient was used as measure of the accuracy of the simulation. The Kappa statistic was lower for spatially uniform wind data (0.5) as compared to spatially varying wind data (0.8) for the two studied events. The results confirm that the use of accurate wind field data is important in fire spread simulation, and can improve its accuracy and the predictive capabilities of the simulator.