The earth landscape is always changing due to human activities and natural phenomena. Therefore, in order to optimize the management of the natural areas, knowledge of the trend and extent of land cover / land use changes is considered necessary, and the estimation of these changes is of great importance. Reviewing these changes through satellite images and predicting and evaluating their potential through modeling can help environmental planners and natural resource managers to make more informed decisions. In the present study, quantitative detection and evaluation of changes in vegetation was performed in the areas with combat desertification projects, Shahdad and Bam in Kerman province and Garmsar in Semnan province, during a 30-year period within 1987, 2002 and 2017. The NDVI vegetation index and land use maps were produced using the ETM + TM and OLI satellite images in the three corresponding periods for the vegetation lands/non-vegetation lands, and agricultural lands. The Kappa coefficient of 0. 83 to 0. 86, 0. 91 to 0. 92, and 0. 94 to 0. 95 was calculated for 1987, 2002, and 2017, respectively, and the total accuracy was between 88 % and 97 %. After providing the land use maps in different years, the monitoring of land use changes was investigated using the change detection methods. According to the trend of changes during the studied periods, our results showed that the vegetation lands in these three areas had an increasing trend, and the non-vegetation lands were turned to vegetation lands over time. Moreover, an increasing trend was found for the agricultural lands during these three periods. Finally, the cost-effectiveness of projects implemented in the studied areas was calculated and evaluated.

Coast is a unique environment in which atmosphere, hydrosphere and lithosphere contact each other. Coastline is one of the most important linear features on the earth’s surface, which display a dynamic nature. Coastal zone, and its environmental management requires the information about coastlines and their changes. This paper examines the current methods of coastline change detection using satellite images. Based on the advantages and drawbacks of the methods, a new procedure has been developed. The proposed procedure is based on a combination of histogram thresholding and band ratio techniques. The study area of the project is Urmia Lake; the 20th. largest, and the second hyper saline lake in the world. In order to assess the accuracy of the results, they have been compared with ground truth observations. The accuracy of the extracted coastline has been estimated as 1.3 pixels (pixel size=30 m). Based on this investigation, the area of the lake has been decreased approximately 1040 square kilometers from August 1998 to August 2001. This result has been verified through TOPEX/Posidon satellite information that indicates a height variation of three meters.

Technological developments and population growth in the past two decades have led to the destruction of nature and variation in vegetation cover to satisfy the ever increasing needs of man. In order to determine the variations in the central regions of Gilan’s vegetation cover GIS and RS were applied. In this research, four satellite images (MSS 1975, TM 1989, ETM 2000 and IRS LISS III 2007) were used to extract NDVI. In addition, based on the distances from villages, buffer layers were produced; afterwards, by using zonal statistics in ArcGIS environment, the density of vegetation cover was determined. The results revealed that in various time periods, as the distance from villages increased the vegetation cover continuously became denser, and within 7000 to 8000 meters, it reached its maximum density.

Remote sensing is the science of obtaining information from the surface of the earth without explicitcontact with the components studied. Commercialization is a set of activities that converts aninnovation into a product or service that brings economic benefits. Given the widespread use formeasurement and the high importance of its application in agriculture, commercialization of thistechnology in agriculture has been a top priority and investigated in this study. The target populationof this research is active and passive companies in this field to use their experience to provide suitablefield for cultivation of Remote sensing technology through in-depth interviewing and snowballsampling. The catch is used. In this research, using product and technology life cycle diagrams,examining the challenges of technology and infrastructure commercialization, commercializationelements, types of software used in the world agricultural industry, Remote sensing investment chartsand analysis The viability of Remote sensing in agriculture as a business has been scrutinized. As aresult, the best way to commercialize the product is to reduce constraints for active companies, buildthe necessary infrastructure, especially timely data, and be independent in deploying this technologyto allow users to use a variety of business methods. Provide.