فصلنامه سنجش از دور و GIS ایران
سال دوازدهم شماره 2 (پیاپی 46، تابستان 1399)

Speckle in Synthetic aperture radar images makes grainy effects, because of the coherent imaging system which cause some difficulties in object-oriented processes, like segmentation or classification. Therefore, a lot of methods have been developed for speckle reduction purpose. These methods can be classified but not limited in some approaches, like spatial based, transform based and optimization, which mostly suffer from limitations like edge and texture destruction and also regulating parameter dependence. In this paper a new structure has been presented based on adaptive filtering in the frequency space, which not only reduces the speckle but also preserves edges and delicate textures. In addition, it has low level of computation and complexity compared to the other methods. Proposed method, improves equivalent number of looks index 50 percent and edge preservation index 50 and 30 percent for real and simulated synthetic aperture radar images, respectively.

Urban development is an inevitable subject due to the increasing population growth in cities. The city consists of open and living systems and a combination of socio-ecological systems. On the one hand, the hurried process of urban growth resulted in the land use change. Subsequently, this manner will damage to the structure, yield, and ecological processes in each city. Meanwhile, the use of ecologic knowledge with the landscape and resilience approaches can help to analyze the present situation and discover the optimal solutions. The resilience in the pattern of the natural structure of the ecological networks depends on the extent and intensity of the green spots. This research was carried out to determine the vegetation changes in Hamadan city between1982 to 2015 to achieve the evaluation of the ecological network structure in the urban development process with a resilience approach. In this study, a conceptual framework derived from ecological knowledge, resilience ideas and the use of modern technologies, such as GIS and RS, was designed to determine the bio-sensitive areas caused by the substructure urban changes. Besides, this study was performed to preserve the remaining biological resources in this area and preventing damage to the natural ecosystem of this city. A series of satellite imagery was classified in four categories such as such as open area, built land, communication paths, vegetation. After the above classification, these maps were processed and analyzed by the TerrSet software and three types of landscape metrics including creating, loosening and separating were reviewed to identify the types of land use changes in these years. The results show that the vegetated area decreased from 2820.2 hectares in 1982 to 1304.2 hectares in 2015 and, on the other hand, built lands and communication paths were degraded from 606.4 hectares in 1982 to 4274.2 hectares in 2015. In general, it can be concluded that this level of change shows a high urban development, decreases in plant vegetation, and its discontinuation so that the above changes have reduced the resilience of the city's ecology network. At the end of the study, numerous strategies were also provided for the restoration of the entire damage in the natural ecological network of Hamadan and its development.

Evapotranspiration is one of the most important components of energy and water balance. The most important way to get real large-scale evapotranspiration is to utilize satellite imagery and remote sensing. Implementation of evapotranspiration calculation algorithms such as SEBAL demands calculation of reference evapotranspiration and thus measuring air temperature, humidity and wind speed. Calculation of evapotranspiration is usually based on obtained information from the nearest weather stations to the study area, which can be error-prone. Therefore, in this study, IoT sensors were used to accurately measure air temperature at 2 m above the ground, as well as air humidity and wind speed in the study area. The study area is the farms of Moghan Agricultural Company in Ardabil province. In this study, 23 nodes were installed in a number of farms. The ground-based energy balance algorithm (SEBAL) was used to calculate the evapotranspiration using Landsat 8 images in 2015.

This study aims to provide a computational-approximate algorithm based on Rationalized Haar (RH) to estimate the vegetation of the Landsat image using reflecting this phenomenon in the near-infrared band. This band is in the RGB color combination and located in the R section. This algorithm, using Digital Number (DN) vegetation in 200 selected pixels of R band (infrared band) from the study area, tries to extract the features and vegetation of the whole study area. The number of selected pixels is distributed uniformly and only covers the vegetation. Due to using the matrix format in the input data, first vegetation reflection matrices for 4 and 8 wavelets are constructed using the assumed 200 pixels. Then, these matrices are extended to 16 and 64 parts respectively, through blocking the Landsat image of the region. Each matrix element represents the average vegetation of the area in its corresponding block. Then, by introducing an efficient mathematical equation, the vegetation of the entire study area is extracted. In addition, each pixel is reconstructed. Due to matrix calculations, speed and accuracy of calculations at the pixel scale will be listed as advantage of this approach. In this study, vegetation extraction with 4 and 8 RH Wavelets was performed with 75 and 87.5% accuracy, respectively. As the number of wavelets increases, the accuracy of the RH wavelet algorithm increases. However, rounding error and the increase in computational cost in high number of wavelet can be listed as disadvantage of this method. Such that, time and space memory will be increased exponentially. In remote sensing, extraction techniques such as classification have been proposed by remote sensing software. The accuracy of vegetation pixel extracted using this approach will be as advantage in comparison with those common methods. In processing and analytical techniques (for vegetation extraction and classification) in remote sensing, many pixels contain vegetation depicted as single or clustered (but in small numbers) while, in other classes such as barren or Urban land will be merged, which RH wavelet overcomes this shortcoming.

Monitoring the earth and its biosphere is an essential task in any scale to achieve a sustainable development. Therefore, forests, as an invaluable natural resource, have an important role to control the climate changes and the carbon cycle. For this reason, biomass and consequently forest height have been known as the key information for monitoring the forest and its underlying surface. Several studies, it has been shown that Synthetic Aperture RADAR (SAR) imaging systems can greatly help to this purpose. In this framework, a novel technique called Polarimetric SAR Interferometry (PolInSAR) is an appropriate and an available tool for forest height estimation, due to its sensitivity to location and vertical distribution of the forest structural components. Based on this, from a view point, the methods employed in this field can be divided into two categories: a) based on Random Volume over Ground (RVoG) inversion model, and b) based on model-based decomposition techniques of PolInSAR data. In this study, in order to improve the forest height estimation, a novel method based on the combination of two mentioned categories has been proposed. The performance and the efficiency of the proposed method were demonstrated by two datasets simulated from the PolSARProSim software in L and P bands. So that, in L band, it obtained 0.69 m, 1.84 m and 3.38 m improvements for height estimation in Digital Elevation Model (DEM) differencing, coherence amplitude and combined (phase and coherence) methods, respectively. As well, in P band, in it obtained 1.53 m and 2.74 m improvements especially in DEM differencing and coherence amplitude methods, respectively.

In recent decades in Iran, due to the effect of climate change and population growth, the extent and depth of water in wetlands have been largely decreased. Therefore, it is worth finding the main reasons of the changes and if possible to reduce the rate of changes. Great advances in remote sensing technology offer valuable opportunities to monitor the trend of changes in natural environment. Landsat satellites from 1970s has the largest archive of remote sensing images. Remote sensing images provide data in wide area with high temporal resolution with low cost. Anzali Wetland is one of the most important international wetlands of Iran which has been registered in Ramsar Convention. In recent decades, population growth and expansion of cities and farm lands near Anzali wetland, climatic changes in this region and also changes in Caspian Sea’s water level changes, this wetland is going to be drained. The present study investigates wetland depth changes using Landsat imagery. Furthermore the depth changes have been thoroughly explained concerning rain fall, temperature and the Caspian Sea’s water level changes over a 30-year period from 1988 to 2018. Our Findings emphasizes that the depth of water depth in this wetland is more related to the changes of the Caspian Sea’s water level and the rainfall and temperature are not the main reason of decreasing of wetland.

The aim of this study was to use the attraction model to increase the spatial resolution of the Digital Elevation Model (DEM) and to use the genetic algorithm to predict stream network in the future and compare its results with stream of extraction of DEM with resolution of 30 m. In the quadrant neighborhood, a neighbor pixel is the only pixel in the same quadrant while in touching neighborhood a neighbor pixel that is the pixel, which physically touches a subpixel. In this method, the pixels were divided into a number of sub-pixels according to the values of the neighboring pixels. The results of the attraction model showed that Scale 2 with the Neighborhood model 2 is more accurate than other Neighborhoods for extracting DEM with higher resolution. The results showed that the predicted stream-network landscapes created using the GLE algorithm had the self-similar tree structure of natural stream networks. Also, the results of the genetic algorithm showed that a change in the degree of waterways in the study area over time compared to the current situation, so that the degree of number of first-class waterways in the future will change to grade 3 due to erosion in upper lands. Therefore, using these models, the condition of waterways can be predicted in the future and better management can be adopted for watersheds.