فصلنامه سنجش از دور و GIS ایران
سال چهاردهم شماره 3 (پیاپی 55، پاییز 1401)

Rice has become one of the most important food security items in many countries, especially Iran. In this study, a model was proposed to select Landsat-8 satellite time-series images in order to prepare a map of paddy lands. The method is based on the phenological characteristics of rice plants and annual surface temperature data from the MODIS sensor. After preprocessing satellite images, they were classified using an object-based approach and fuzzy functions. Various data such as a digital elevation model, land surface temperature, and spectral indices including NDVI, EVI, NDBI and LSWI are used to improve the classification process. In addition, information about the segmentation of the image is employed during the process of classification. Because of the different traits of paddy fields, a digital elevation model with a resolution of 12.5 meters was used to help differentiate paddy lands from other vegetation. In addition, a comparison was made between the results of classification based on object-based and pixel-based methods. The results showed that the object-based classification yields better results than the pixel-based method with special considerations. The classification result following validation using ENVI software pixel-based classification indicated an overall accuracy of 92 percent and a kappa value of 0.89. This is in contrast to the object-based classification technique in the eCognition software, which yielded an overall accuracy of 94 percent and a kappa coefficient of 0.92.

The design of Remote Sensing algorithms and the development of various methods of processing satellite images to identify porphyry copper deposits are among the important topics of studies in the field of mineral resource evaluation and their optimal exploitation. To this end, the determination of alteration zones provides a suitable tool for designing acceptable exploratory patterns. In this research with an almost comprehensive strategy and using the determination of alterations related to porphyry copper deposit based on Lowell and Gilbert model with three different strategies (visual, spectral and statistical processing) as well as the extraction of linements in the case area The study suggested the concentration range of the mineral for drilling. The study area in this article is Masjed Daghi porphyry copper deposit in the northeast of East Azerbaijan province, which consists of multispectral satellite images of ASTER, OLI of Landsat-8 and Sentinel-2 sensors for various processes including band ratio combinations, principal component analysis and pixel and subpixels basics spectral processing methods including SAM and MTMF, and statistical processing using the logical operator algorithm. Finally, by fuzzy and combining the layers of satellite image processing with geometric structures of the region (linements) which were extracted on Sentinel-2 data in two automatic and semi-automatic methods, the results were analyzed in GIS space and by comparing the presented results with the analysis of ground samples, the accuracy and conformity of the target areas were confirmed. User and producer accuracy for the area with the first priority were 78.54% and 78.36%, respectively, which are more appropriate criteria for introducing the area of ​​the drilling center.

Synthetic aperture radar (SAR) sensors with various properties offer potential in various remote sensing applications, such as land cover and land use segmentation. Despite the two independent approaches of region-based segmentation and boundary-based segmentation, it isn't easy to obtain satisfactory results if either process is used in SAR images. In contrast, complementary information can be obtained using both region-based and boundary-based segmentation methods, removing existing limitations and improving results. In this research, with the help of polarimetric SAR images, a new segmentation method is presented, aiming to improve segmentation results by combining the two region-based and boundary-based approaches. From the set of superpixel methods, the Felzenszwalb method as a proposed region-based algorithm is compared with Quickshift and SLIC methods. The proposed method was able to prevent over-segmentation of the image and significantly increased the efficiency of segmentation analysis. Also, as the proposed method of boundary-based segmentation, Shannon entropy has considerably preserved the boundaries of the image segmentation compared to the two gradient-based methods, Canny and Laplacian. Comparison of the results of this method with reference data shows the total error of 10.39% and 11.25% for the first and second-time images, respectively. Compared to the performance of the other two methods, the absolute error has been decreased to 5.81% and 9.73% in the first image, and 11.16% and 13.86% in the second image, respectively. Finally, as a significant achievement of this research, integrating the two proposed segmentation algorithms improves the accuracy of polarimetric image segmentation.

In recent years, maritime and aerial surveillance have become commonplace for marine pollution control; however, these methods alone cannot provide rapid and systematic monitoring due to the limitations of weather conditions, time, and location. In this regard, satellite remote sensing can play an important role in the initial detection and continuous monitoring of oil spills at sea. The synthetic aperture radar (SAR) sensor is an active microwave sensing system that can be used for oil spill detection, along with optical sensors such as MODIS, with simultaneous imaging capability. The aim of this study was to detect the oil spills around oil platforms in the northern part of the Persian Gulf on June 15, and 17, 2015, using MODIS thermal infrared imagery and Sentinel-1 images. To estimate the sea surface temperature, the split-window algorithm was applied to band 20 of MODIS. Results showed that the sea surface covered by oil spill has lower temperature than surroundings. For accurate detection of oil slicks and accuracy assessment of the results of applied image processing method on the MODIS data, the Sentinel-1 vertical polarization image and noise removal processes such as filtering and multi-looking were used. Finally, by comparing the field temperature measured by Boushehr marine waveguide and the temperature estimated for the MODIS image, and review of the geographical location of detected oil slicks, the accuracy of the results of this study and the applied image processing methods were confirmed. Application of MODIS band 20 aiming the extraction of sea-surface temperature, and its thermal infrared bands for oil spill detection at sea surface are evaluated in this study for the first time.

In recent years, maritime and aerial surveillance have become commonplace for marine pollution control; however, these methods alone cannot provide rapid and systematic monitoring due to the limitations of weather conditions, time, and location. In this regard, satellite remote sensing can play an important role in the initial detection and continuous monitoring of oil spills at sea. The synthetic aperture radar (SAR) sensor is an active microwave sensing system that can be used for oil spill detection, along with optical sensors such as MODIS, with simultaneous imaging capability. The aim of this study was to detect the oil spills around oil platforms in the northern part of the Persian Gulf on June 15, and 17, 2015, using MODIS thermal infrared imagery and Sentinel-1 images. To estimate the sea surface temperature, the split-window algorithm was applied to band 20 of MODIS. Results showed that the sea surface covered by oil spill has lower temperature than surroundings. For accurate detection of oil slicks and accuracy assessment of the results of applied image processing method on the MODIS data, the Sentinel-1 vertical polarization image and noise removal processes such as filtering and multi-looking were used. Finally, by comparing the field temperature measured by Boushehr marine waveguide and the temperature estimated for the MODIS image, and review of the geographical location of detected oil slicks, the accuracy of the results of this study and the applied image processing methods were confirmed. Application of MODIS band 20 aiming the extraction of sea-surface temperature, and its thermal infrared bands for oil spill detection at sea surface are evaluated in this study for the first time.