Original Article
Mahmoud Bayat; Khosro Mirakhorloo; Hosein Sadeghzadeh; Sahar Heidari Masteali
Abstract
Lack of up-to-date, documented and scientific information on the current situation (area and distribution) of Zanjan poplar plantation is one of the main problems of wood production managers for planning and management of wood supply in the province and the country. In this study, Sentinel-2 satellite ...
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Lack of up-to-date, documented and scientific information on the current situation (area and distribution) of Zanjan poplar plantation is one of the main problems of wood production managers for planning and management of wood supply in the province and the country. In this study, Sentinel-2 satellite data with spatial resolution of 10 m in spectral bands were used and the ground truth map of existing poplar fields with 600 points was plotted in all cities and villages from field surveys. From the beginning to the end of the poplar growing season (first half of March to December 2018), at least 6 time periods of 30 to 40 days were used in the SVM classifier. Post-test and calibration of SVM model based on the phenology of poplar genus and field samples were extracted, populated area distribution map of province was extracted. The results showed that the total area of poplar areas is 2744 hectares which covers 0.12% of total area of Zanjan province. One percent of the total polygons were randomly selected for field control and after field control, the overall mapping error was obtained and calculated. In this study, the exact location and area of poplar mills were estimated with acceptable accuracy (96%). So that using extracted information (distribution map of poplars of the province) can provide studies on comprehensive planning of poplars and sustainable management of wood production from the poplars of the province.
Original Article
Amir Hedayati; Mohammad H Vahidnia; Hosseain Aghamohammadi
Abstract
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 ...
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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.
Original Article
alireza mahmoodi; Marzieh Mokarramb
Abstract
Today, remote sensing is used for plant studies, such as determining nutrient levels, plant diseases, water deficiency or excess, weed identification, and so on. As electromagnetic waves strike the plants, they react in different ways (absorption, reflection or passage) based on the characteristics of ...
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Today, remote sensing is used for plant studies, such as determining nutrient levels, plant diseases, water deficiency or excess, weed identification, and so on. As electromagnetic waves strike the plants, they react in different ways (absorption, reflection or passage) based on the characteristics of the plants. The quantity of nutrients in a plant can be determined through measurement science in plant studies. Since the amount of nutrients in the plant can be determined, it is possible to know how much fertilizer the plant needs. On the other hand, identified the nutrients in the plant, especially rangeland plants. A spectrometer was used to measure the plant's response to electromagnetic waves in the range of 0.3 to 1.1 m. Following that, the relationship between the amount of electromagnetic waves and the amount of nutrients in these plants was determined. The results showed that in Fagonia bruguieri b1026 nm, in Peganum harmala b1040 nm, in Ziziphus spina-christi b1046 nm, in Tecurium persicum band 1030 nm, in Vitex pesedo-negundo b400 and b1038 and in Otostegia persica band They are effective in predicting the value of P. For the prediction of Zn in F. bruguieri b1026 nm band, in P.harmala b1040 nm band, in Z. spina-christi ba1045 nm band, in T. persicum pea b1030 nm band, in V. pesedo-negundo plant b1010 nm and in O. persica band They are the most effective bands. To predict Cu, it is determined using spectral band values that in F.bruguieri band is b402 nm, in P. harmala band is b410 nm, in Z. spina-christi band is b1046 nm, in T. persicum band is b1030 nm, in V.pesedo and O. persica b1038 are the most effective bands.
Original Article
MayamSadat Ahmadi; Abbass Malian
Abstract
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 ...
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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.
Original Article
Marjan Teheri; MahmodReza Sahebi; Mehrnoosh Omati
Abstract
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 ...
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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.
Original Article
Majid H.tangestani; Marjan Karimi
Abstract
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 ...
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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.
Original Article
Reza Shahhoseini; Kamal Azizi; Arastou Zarei; Fatemeh Moradi
Abstract
Land use maps describe the spatial distribution of natural resources, cultural landscapes, and human settlements that are essential for decision-makers. Therefore, the accuracy of maps obtained from the classification of satellite images is very effective in uncertainty for urban management. Due to the ...
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Land use maps describe the spatial distribution of natural resources, cultural landscapes, and human settlements that are essential for decision-makers. Therefore, the accuracy of maps obtained from the classification of satellite images is very effective in uncertainty for urban management. Due to the uniform quality of images in large areas at regular intervals, remote sensing images are essential for land use maps. The primary purpose of this study is to present a proposed method to create an accurate land cover map in urban areas using a combination of Sentinel-1 and Sentinel-2 data. For this purpose, the features of the backscattering coefficient VV and the two parameters obtained from the H-α decomposition method (entropy, alpha) of Sentinel-1 radar images and the features of the blue, green, red band, NDVI, NDWI, MNDWI, and SWI were extracted from Sentinel-2 Multispectral images and used as influential components to classify the urban area. To separate agricultural areas from other coatings, the SWI index was used. Elevation data have also been used to optimally distinguish complex classes with different topographies. We evaluated the extraction of effective indicators from these two datasets in an object-oriented approach based on support vector machine algorithms and random forest for land use classification. The results showed that using properties extracted from radar and Multispectral images simultaneously in the object-oriented classification method could altogether determinate the object's properties in the study area. When optical and radar data were used simultaneously for both classification algorithms, the overall accuracy classification increased. For the stochastic forest method, which provided the highest accuracy, the overall accuracy for the radar and optics data combination approach increased by 13% and 5%, respectively, compared to the radar feature approach and the optics feature approach alone. There was also a significant difference in classification accuracy at all levels between the support vector machine classification algorithm and the random forest. The results showed that the random forest classification method's overall accuracy and support vector machines were 83.3 and 79.8%, respectively, and the kappa coefficient was 0.72 and 0.68%, respectively.
