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.

This paper attempts to shed light on the current role of academia in the context of rural areas of low population density, which are regional interaction models. In this study, we follow a qualitative research methodology of a case study. We found that through the case study applied to a hotel unit, that the Academia can through its third mission, and in the context of regional triple helix dynamics (Academia-Business-government interaction), play an important role in terms of knowledge dissemination, wealth creation and employability. The limitations, which our study presents, are principally related to the measurement of the variables. Some of the characteristics of education should be studied more deeply. In the instance of a case study applied to the hospitality industry, it is important to take as limitations of the study to its direct application to any economic context. This study allowed however, contribute to the enrichment of literature through case studies presented in the hospitality industry.

The landlocked and fragile countries’ ability to create a sustainable path to economic growth and poverty reduction is inextricably linked to their export diversification potential, itself related to their connectivity within themselves, in the region, and other external markets. Mali, Chad, and Niger are first challenged by their geography—their landlocked nature with their vast and thinly populated space serves to isolate the most vulnerable communities from external and internal markets. Adding to these geographic disadvantages non-landlocked is incentive environment—defined by high and variable customs common external tariff regimes resulting from multiple overlapping regional trade arrangements—places a wedge between domestic and international prices, provides a disincentive to exports in favor of non-tradable and domestic-oriented sectors. By bringing greater coherence and convergence between the many common external tariff regimes in operation and the rationalization of their structures, and improving connectivity within and between markets, Mali, Chad, Niger, and Guinea can better promote the reallocation of resources toward tradable goods and services, putting the countries on a path toward greater economic inclusion and sustainable growth.

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.