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.

Pan Sharpening is normally applied to sharpen a multispectral image with low resolution by using a panchromatic image with a higher resolution, to generate a high resolution multispectral image. The present study aims at assessing the power of Pan Sharpening on improvement of the accuracy of image classification and land cover mapping in Landsat 8 OLI imagery. In this respect, different Pan Sharpening algorithms including Brovey, Gram-Schmidt, NNDiffuse, and Principal Components were applied to merge the Landsat OLI panchromatic band (15 m) with the Landsat OLI multispectral: visible and infrared bands (30 m), to generate a new multispectral image with a higher spatial resolution (15 m). Subsequently, the support vector machine approach was utilized to classify the original Landsat and resulting Pan Sharpened images to generate land cover maps of the study area. The outcomes were then compared through the generation of confusion matrix and calculation of kappa coefficient and overall accuracy. The results indicated superiority of NNDiffuse algorithm in Pan Sharpening and improvement of classification accuracy in Landsat OLI imagery, with an overall accuracy and kappa coefficient of about 98.66% and 0.98, respectively. Furthermore, the result showed that the Gram-Schmidt and Principal Components algorithms also slightly improved the accuracy of image classification compared to original Landsat image. The study concluded that image Pan Sharpening is useful to improve the accuracy of image classification in Landsat OLI imagery, depending on the Pan Sharpening algorithm used for this purpose.

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.

In the present study, the suitability of principal components analysis (PCA) based techniques was evaluated for identification of geological units from Landsat-9 satellite imagery. In this respect, a scene of Landsat-9 operational land imager 2 (OLI–2) data of the year 2023 was acquired and a geological map scale 1:100000 of the study area was used as the reference. The results indicated suitability of the PCA based techniques for discrimination of geological units from Landsat-9 image, especially the PCA of decorrelation stretch (DS) approach. The PCA-DS approach, which considered the advantages of both PCA and DS techniques, successfully identified all the geological units in the study area, including the Basalt, Sandstone, Dolomite, and Conglomerate. However, the performance of the PCA and DS techniques was also reasonable for this purpose. On the other hand, the study revealed weak performance of the minimum noise fraction (MNF) and PCA-MNF techniques for geological mapping using Landsat-9 imagery. In conclusion, the study demonstrated the advantage of the PCA-DS approach for geological mapping using Landsat-9 imagery; therefore, it may be useful in futures studies for geological mapping along the whole Alborz Mountain with similar lithological and geomorphological conditions.