این آزمایش با هدف بررسی تاثیر هیومی پتاس در افزایش سطح تحمل به شوری گیاه پوششی فیلا (Phyla nodiflora L.) بر اساس ویژگی های مورفوفیزیولوژیک انجام شد. طرح کرت های خردشده در یک آزمایش گلخانه ای با دو عامل به صورت بلوک های کامل تصادفی در سه تکرار اجرا شد. کرت اصلی شامل شوری کلرید سدیم در 5 سطح مختلف (صفر، 4، 8، 12 و 16 دسی­زیمنس بر متر) بود؛ در حالی که، کرت فرعی شامل سه سطح هیومی پتاس (صفر، 500 و 1000 میلی­گرم) بود. نتایج نشان داد، بدون در نظر گرفتن تیمار کودی، وزن تر شاخساره و کیفیت ظاهری در تیمار شوری 16 دسی زیمنس بر متر در مقایسه با گیاهان شاهد به ترتیب کاهش معنی دار 02/19 و 34/24% نشان دادند. دیگر ویژگی مثبت گیاه فیلا در تنش شوری 16 دسی زیمنس بر متر، وضعیت به نسبت مطلوب رنگدانه های گیاهی بود. افزون بر این، کیفیت ظاهری همبستگی قوی و مثبتی با طول شاخساره، وزن تر و خشک شاخساره و وزن تر ریشه نشان داد. به طور کلی نتایج بیانگر آن بود که فیلا در زمان تنش شوری از ویژگی های رشدی خود کاسته است. بدین ترتیب از کیفیت ظاهری در شوری بالا تا حدودی کاسته شد؛ اما در عوض سبزینگی فیلا در شرایط تنش حفظ شد. بر اساس نتایج وزن تر و خشک شاخساره و ریشه، محتوای نسبی آب (RWC) برگ و کیفیت ظاهری گیاه فیلا تا سطح شوری 8 دسی زیمنس بر متر از وضعیت مطلوبی برخودار بود و نیازی به استفاده از تیمار هیومی­پتاس تا این سطح از تنش وجود ندارد. در سطوح شوری بالا (12 و 16 دسی زیمنس بر متر)، ویژگی های مورفوفیزیولوژیک فیلا کاهش یافت. در نتیجه در سطح شوری بالا برای بهبود وضعیت کلی گیاه، کاربرد هیومی پتاس پیشنهاد می شود. به طوری که، هیومی پتاس 500 میلی گرم بر لیتر سبب افزایش طول شاخساره، تعداد شاخساره جانبی و RWC در سطح شوری 16 دسی زیمنس بر متر شد. افزون بر آن، 1000 میلی گرم بر لیتر هیومی پتاس، سبب بهبود رنگدانه های گیاهی در تیمار شوری 12 دسی زیمنس بر متر گردید.

The population growth and the development of the urban environments have created a lot of incentives for researchers in the field of spatial information to provide methods for extracting features. Remote sensing technology and satellite imagery have become an important tool for obtaining information in order to extract features. The presence of some obstacles such as weather conditions and the presence of clouds and shadows in satellite imagery prevents us from getting information from the surface of the earth. To solve this problem, we examined the ability of the radar images in helping to extract the urban features by optical images, especially detecting the pixels located in shadow and cloud areas. In this paper, the images of WorldView-3, ALOS-2 with single polarization and four polarization were taken into consideration and the optimally extracted features were used to classify the vegetation, building, road and soil using feature and decision level fusion. The optical features include the gray-level co-occurrence matrix (GLCM) and the radar features include GLCM for the image of single polarization, the features of the target decomposition, the separation characteristics and the main characteristics for the image with full polarization. In the classification by optical and radar features using feature level fusion, the combined accuracy of 83. 96 percent was obtained and somewhat it was able to correctly identify the pixels in the shadow and cloud areas, while the classification with the optical features obtained a total accuracy of 81. 02 percent. The obtained results of the decision level fusion are very low and unacceptable. The results in this paper showed that the use of the radar images along with the optical images in the feature classification using the feature level fusion improved the accuracy to some extent,and depending on the different conditions the result may be different.

Recently, a new mode is proposed in Dual Polarimetry (DP) imaging systems that is called Compact Polarimetry (CP) which has several important advantages in comparison with Full Polarimetry (FP) such as reduction ability in complexity, cost, mass, and data rate of a Synthetic Aperture RADAR (SAR) system. Despite these advantages, the CP mode, compared to the FP mode, still achieves less information to be extracted from targets. Therefore, accuracies of classification obtained from CP data are lower than those obtained from FP data. In this paper, a new method is proposed to improve the results of classification obtaind by using CP data. For this propose, two ways are considered. First, the CP modes simulated by RADARSAT-2 FP mode, and second, Pseudo Quad Polarimetry (PQ) modes reconstructed by exploited CP modes are combine in the extracted polarimetric feature level. Results of this study show that this combination can be increase the classification accuracies.

Achieving satellite images with high simultaneously spatial-temporal resolution has been one of the serious challenges faced by researchers in the field of remote sensing and its applications. In recent years, researchers have made serious efforts to solve the problem. In this study, producing Landsat like land surface temperature images with less than 16 day temporal resolution and over different land covers, using spatio-temporal image fusion algorithm (STI-FM) and MODIS Land surface temperature images, was investigated. The STI-FM technique consist of two main steps. First establishing a linear relationship between two consecutive MODIS LST images acquired at time 1 and time 2; then utilizing the above mentioned relationship as a function of a Landsat-8 LST image acquired at time 1 in order to predict a synthetic Landsat-8 LST image at time 2. The results showed strong linear relationship between the two consecutive MODIS images at times 1 and 2 (R2 in the range 0. 85-0. 95). The synthetic LST images were evaluated qualitatively and quantitatively and it was found that there is a high visual and strong agreements with the actual Landsat-8 LST images over different land covers. For example R2 and RMSE values were ranged 0. 74-0. 94 and 1. 44-2. 52, respectively.

Anzali Wetland in Iran as one of the most valuable wetlands registered in the Ramsar Convention is being destroyed by environmental factors and human activities. In the last two decades, among various satellite images, radar images have played a special role in wetland monitoring. Radar is an all-weather sensor and it is sensitive to surface roughness and moisture, they serve as a valuable source for quick and accurate monitoring of wetlands. However, similarities in backscattering coefficients of different wetland classes and relatively difficult processing –,in comparison to optical images-are the most important factors that limit their application. In this study, the capabilities of SAR images in the classification of Anzali wetland and the three main land use classes around the wetland (i. e. agricultural lands, reeds, and built-up areas) were evaluated. Two radar images,Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar (ALOS/PALSAR) and Sentinel 1 captured in 2018 were used. The texture parameters of the two images have been extracted. The images and their extracted texture layers have been fused by the feature-level method and further classified by the random forest method. The overall accuracy of feature-level fusion is equal to 75% and the kappa coefficient is equal to 0. 62. The evaluation results related to producer and user accuracy are 100% and 83. 33%, respectively, show the high capability of radar images in the classification and detection of wetlands. However, some errors have been observed in the separation of agricultural lands, reeds, and built-up areas.

Increasing development of urban areas, the need for various information from the urban environment, and also technological advancements have increased the importance of automatic and semi-automatic classification and identification of this type of land cover. The diversity of remote sensing data have created a wide scope for urban feature detection. Moreover, by launching satellite sensors with a spatial resolving power of less than 1 meter, a dramatic revolution has occurred in the tendency of remote sensing researchers toward classification of urban features. The existence of various features and different applications of spatial information in urban areas have made it possible to integrate various data sources with the aim of identifying different urban features. The present study seeks to integrate optimal properties extracted from optical and LiDAR data in order to identify urban features in the study area. In this regard, colored features, normal difference vegetative index (NDVI), first-order statistical texture in three windows of 5×5, 7×7 and 9×9, second-order statistical texture in three windows of 7×7, 11×11 and 15×15 extracted from the multispectral optical data were calculated along with features of normalized difference index (NDI), slope, slope direction, profile curve, surface curve, roughness, variance, laplacian, smoothness and normalized digital surface model (nDSM) extracted from the LiDAR data. Since increased amount of information has made the process of identifying features in the region time-consuming, the present study applies intelligent genetic algorithm to select optimal features from the calculated features. A total number of 361 features were produced from this data, including 9 colored features, a vegetation index, 144 first-order statistical texture, and 192 second-order statistical texture from multispectral optical data and 14 features from LiDAR data. Then, 17 features including seven features of the LiDAR data and 10 features of the multispectral optical data were determined using genetic algorithm as the optimal features for more appropriate identification of urban features. Finally, support vector machine (SVM) classification method was used to identify the desired features. Results indicate that compared to LiDAR data, multispectral optical data have a better performance in classifying vegetation features, while LiDAR data have been more suitable for the classification of road and building features. In other words, multispectral optical data work appropriately in identifying features with different radiometric information, while classification of features with similar radiometric information, such as roads and buildings is problematic. Thus, LiDAR elevation data help in identification of these features. Additionally, using optimal features along with the primary bands have increased the accuracy of urban features classification. Using optimal features and initial data, the accuracy of support vector machine algorithm classifier in the study area is calculated to be 88. 734, which shows 25. 438% improvement compared to the initial multispectral optical data classification, and 18. 236% improvement compared to the initial LiDAR data classification.