Identify and diagnosis of homogenous units and separating them and eventually planning separately for each unit are considered the most principled way to manage units of forests and creating these trustable maps of forest’s types, plays important role in making optimum decisions for managing forest ecosystems in wide areas. Field method of circulation forest and Parcel explore to determine type of forest require to spend cost and much time. In recent years, providing these maps by using digital classification of remote sensing’s data has been noticed. The important tip to create these units is scale of map. To manage more accurate, it needs larger scale and more accurate maps. Purpose of this research is comparing observed classification of methods to recognize and determine type of forest by using data of Land Cover of Modis satellite with 1 kilometer resolution and on images of OLI sensor of LANDSAT satellite with 30 kilometers resolution by using vegetation indicators and also timely PCA and to create larger scale, better and more accurate resolution maps of homogenous units of forest. Eventually by using of verification, the best method was obtained to classify forest in Golestan province’s forest located on north-east of country.

Excessive usage of chemicals in crops, especially in leafy vegetables, caused people exposed to health and environmental risks. In Iran, spinach used as a winter vegetable that believed has high Iron and is useful for anemia. The objective of the experiment was to determine the optimum use of each macronutrients to obtain safe maximum growth and yield for scaling up among farmers. Treatments were chemical fertilizers including ammonium sulfate, triple superphosphate and potassium sulfate at 50, 100, 150 and 200 kg/h against control in a randomized complete block design. Results showed that nitrogen caused elevation of fresh and dry weight in spinach as the maximum obtained in 200 kg/h ammonium sulfate. Results obtained from effect of phosphorus showed that super phosphate increased fresh and dry weight of spinach; but potassium sulfate had no effect on its growth and yield. Analysis of variance on cross effect of data showed significant differences in fresh and dry weight, number of leaves, chlorophyll content and nitrate, and non-significant differences in length and wide of leaves.

Every year, hundreds of fires occur in the forests and rangelands across the world and damage thousands hectare of trees, shrubs, and plants which cause environmental and economic damages. This study aims to establish a real time forest fire alert system for better forest management and monitoring in Golestan Province. In this study, in order to prepare fire hazard maps, the required layers were produced based on fire data in Golestan forests and MODIS sensor data. At first, the natural fire data was divided into two categories of training and test samples randomly. Then, the vegetation moisture stresses and greenness were considered using six indexes of NDVI, MSI, WDVI, OSAVI, GVMI and NDWI in natural fire area of training category on the day before fire occurrence and a long period of 15 years, and the risk threshold of the parameters was considered in addition to selecting the best spectral index of vegetation. Finally, the model output was validated for fire occurrences of the test category. The results showed the possibility of prediction of fire site before occurrence of fire with more than 80 percent accuracy.

To achieve sustainable development, detailed planning, control and management of land cover changes that occur naturally or by human caused artificial factors, are essential. Urban managers and planners need a tool that represents them the information accurate, fast and in exact time. In this study, land use changes of 3 periods, 1994-2002, 2002-2009, 2009-2015 and predictions of 2009, 2015 and 2023 were assessed. In this paper, Maximum Likelihood method was used to classify the images, so that after evaluation of accuracy, amount of overall accuracy for images of 2013 was 85.55% and its Kappa coefficient was 80.03%. To predict land use changes, Markov-CA model was used after assessing the accuracy, and the amount of overall accuracy for 2009 was 82.57% and for 2015 was 93.865%. Then web GIS application was designed via map server application and evoked shape files through map file and open layers to browser environment and for design of appearance of website CSS, HTML and JavaScript languages were used. HTML is  responsible for creating the foundation and overall structure of webpage but beautifying and layout design on CSS. 

Dust is one of the atmospheric pollutants that have adverse environmental effects and consequences. Dust fall contains particles of 100 microns or even smaller ones, which fall from the atmosphere onto the earth surface. The aim of this study is to determine the concentration of lead in dust fall samples in order to study the pollution level of this element in Zahedan, Sistan and Baluchistan Province, Iran. Therefore, sampling was carried out using 30 marble dust collectors (MDCO) for 3 months in the spring of 2015 to investigate the quantitative variation and spatial analysis of lead content in dust fall. These dust collectors were placed at 30 stations on the building roofs with a height of approximately 1.5 meters across the city. According to the results, the mean lead concentration in the spring was 90.16 mg/kg. In addition, the zoning map of lead content shows that the lowest level of lead was measured at Imam Khomeini station while the highest amount of lead appeared in Mostafa Khomeini station.