Lake Urmia is the 20th largest lake and the second largest hyper saline lake (before September 2010) in the world. It is also the largest inland body of salt water in the Middle East. Nevertheless, the lake has been in a critical situation in recent years due to decreasing surface water and increasing salinity. This study modeled the spatiotemporal changes of Lake Urmia in the period 2000–2013 using the multi-temporal Landsat 5-TM, 7-ETM+ and 8-OLI images. In doing so, the applicability of different satellite-derived indexes including Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Normalized Difference Moisture Index (NDMI), Water Ratio Index (WRI), Normalized Difference Vegetation Index (NDVI), and Automated Water Extraction Index (AWEI) were investigated for the extraction of surface water from Landsat data. Overall, the NDWI was found superior to other indexes and hence it was used to model the spatiotemporal changes of the lake. In addition, a new approach based on Principal Components of multi-temporal NDWI (NDWI-PCs) was proposed and evaluated for surface water change detection. The results indicate an intense decreasing trend in Lake Urmia surface area in the period 2000–2013, especially between 2010 and 2013 when the lake lost about one third of its surface area compared to the year 2000. The results illustrate the effectiveness of the NDWI-PCs approach for surface water change detection, especially in detecting the changes between two and three different times, simultaneously.

Determining optimum locations for an industry is a critical and complex decision for manufacturers. To select a potential site to establish food industries, various criteria should be considered regarding accessibility, delivery, market, and environmental factors. In this study, the weighted overlay method was employed to develop an up-to-date geodatabase providing the potential areas for establishing food industries to assist urban development in Qaemshahr city, Iran. The required data were acquired through various sources including remote sensing, Global Positioning System (GPS), and Geographical Information System (GIS). To achieve the aim, firstly the criteria of food industries settlement in Qaemshahr city have been measures, and the corresponding maps have been prepared. Using weighted overlay method all weighted maps were aggregated and final suitability map was established. Finally, the suitable sites for food industries in Qaemshahr were determined. The results have been validated by experts practicing Qaemshahr urban development. The results showed that around 172 sites are suitable for establishing the food industries in Qaemshahr. Accuracy assessment analysis proved the effectiveness of the achieved results with the accuracy level of 90%. The study concluded that the geodatabase model prepared in this study could be useful for future food industry development plans in Qaemshahr city, Iran.