A global site selection for astronomy was performed with 1 km spatial resolution (∼ 1 Giga pixel in size) using long term and up-to-date datasets to classify the entire terrestrial surface on the Earth. Satellite instruments are used to get the following datasets of Geographical Information System (GIS) layers: Cloud Coverage, Digital Elevation Model, Artificial Light, Precipitable Water Vapor, Aerosol Optical Depth, Wind Speed and Land Use -Land Cover. A Multi Criteria Decision Analysis (MCDA) technique is applied to these datasets creating four different series where each layer will have a specific weight. We introduce for the first time a "Suitability Index for Astronomical Sites" namely, SIAS. This index can be used to find suitable locations and to compare different sites or observatories. Mid-western Andes in South America and Tibetan Plateau in west China were found to be the best in all SIAS Series. Considering all the series, less than 3 % of all terrestrial surfaces are found to be the best regions to establish an astronomical observatory. In addition to this, only approximately 10 % of all current observatories are located in good locations in all SIAS series. Amateurs, institutions or countries aiming to construct an observatory could create a short-list of potential site locations using layout of SIAS values for each country without spending time and budget. The outcomes and datasets of this study has been made available through a web site, namely "Astro GIS Database" on www.astrogis.org.

A site selection of potential observatory locations in Turkey have been carried out by using Multi-Criteria Decision Analysis (MCDA) coupled with Geographical Information Systems (GIS) and satellite imagery which in turn reduced cost and time and increased the accuracy of the final outcome. The layers of cloud cover, digital elevation model, artificial lights, precipitable water vapor, aerosol optical thickness and wind speed were studied in the GIS system. In conclusion of MCDA, the most suitable regions were found to be located in a strip crossing from southwest to northeast including also a diverted region in southeast of Turkey. These regions are thus our prime candidate locations for future on-site testing. In addition to this major outcome, this study has also been applied to locations of major observatories sites.Since no goal is set for the best, the results of this study is limited with a list of positions. Therefore, the list has to be further confirmed with on-site tests. A national funding has been awarded to produce a prototype of an on-site test unit (to measure both astronomical and meteorological parameters) which might be used in this list of locations.

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Percent tree cover is the percentage of the ground surface area covered by a vertical projection of the outermost perimeter of the plants. It is an important indicator to reveal the condition of forest systems and has a significant importance for ecosystem models as a main input. The aim of this study is to estimate the percent tree cover of various forest stands in a Mediterranean environment based on an empirical relationship between tree coverage and remotely sensed data in Goksu Watershed located at the Eastern Mediterranean coast of Turkey. A regression tree algorithm was used to simulate spatial fractions of Pinus nigra, Cedrus libani, Pinus brutia, Juniperus excelsa and Quercus cerris using multi-temporal LANDSAT TM/ETM data as predictor variables and land cover information. Two scenes of high resolution GeoEye-1 images were employed for training and testing the model. The predictor variables were incorporated in addition to biophysical variables estimated from the LANDSAT TM/ETM data. Additionally, normalised difference vegetation index (NDVI) was incorporated to LANDSAT TM/ETM band settings as a biophysical variable. Stepwise linear regression (SLR) was applied for selecting the relevant bands to employ in regression tree process. SLR-selected variables produced accurate results in the model with a high correlation coefficient of 0.80. The output values ranged from 0 to 100 %. The different tree species were mapped in 30 m resolution in respect to elevation. Percent tree cover map as a final output was derived using LANDSAT TM/ETM image over Goksu Watershed and the biophysical variables. The results were tested using high spatial resolution GeoEye-1 images. Thus, the combination of the RT algorithm and higher resolution data for percent tree cover mapping were tested and examined in a complex Mediterranean environment.

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