فصلنامه سنجش از دور و GIS ایران
سال شانزدهم شماره 3 (پیاپی 63، پاییز 1403)

Plant phenology plays an important role in plant ecosystems and serves as a key indicator of ecological changes. With the expansion of urbanization, urban green spaces have become increasingly important in residential areas. On the other hand, The use of plants in urban settings and the green space services they provide have garnered significant attention in recent studies. The value of urban green spaces has been recognized for their numerous benefits to human health and the ecological environment of cities. Therefore, it is essential to study and monitor the phenological cycles of plants in urban areas at various spatial-temporal scales, considering their pivotal role in the urban ecosystem and society health.

This study utilized the widely used NDVI and EVI indices calculated from Landsat satellite OLI sensor and MOD13Q1 product of MODIS sensor images to investigate the plant phenology cycle in the Ahvaz metropolitan area from 2015 to December 2019. Satellite images were retrieved and processed using the Google Earth Engine platform. The phenological cycle of plants was obtained based on the vegetation indices, categorized according to vegetation type and compared with the phenological cycle obtained from the ground survey data. Due to probability noise and pixels with spectral mixing, a Savitzky-Golay filter was applied to smooth the phenological cycle of plants.

The results indicate the increasing trend in the values of both NDVI and EVI indices annually, with a rise of 0.03 and 0.04 in the OLI sensor and 0.01 in the MOD13Q1 product, respectively. These positive changes were particularly noticeable in January, March, October, November, and December for both sensors. Variations in plant phenology phases were observed between the two sensors, with the most significant differences occurring in 2018 and 2019. This shows that under favorable weather conditions, there is an increase in plant chlorophyll content, leading to disparities between the results of the two sensors. The transition periods of the growing season identified by the OLI sensor exhibited more detail compared to the MODIS medium resolution dataset. Although the MODIS sensor indicated an earlier start to the growing season than the OLI sensor, the shape of the phenological cycle curves from both sensors appeared similar despite discrepancies in their start and end dates.Generally, according to the MODIS product, the duration of the growing season (between mid-winter and early summer) is approximately four months. These disparities point to more changes in vegetation that can be better detected using high-resolution images compared to sensors with medium and low spatial resolution. Overall, significant changes in the phenological cycle of plants were observed at the urban area level, reflecting various ground phenomena that contribute to increased heterogeneity in satellite sensor image pixels.

The smallest difference between the periods of the growing season of plants observed by ground observations in OLI and MODIS sensors was 7 and 10 days for the Start of Growing Season (SOS), respectively. The largest difference was noted at the peak of the growing season, with disparities of 20 and 35 days, and for the End of Growing Season (EOS), 20 days later and 20 days earlier, respectively, based on ground observations. However, the length of the growing season (LOS) in the OLI sensor is approximately five months, indicating closer alignment with ground observations. This divergence is attributed to increased heterogeneous conditions in the target phenomena and/or the spatial resolution of MODIS sensor images.it is evident that the results obtained from the OLI sensor enhance our understanding of human interactions with the natural environment in urban areas. Addressing these findings in future studies can help mitigate environmental challenges and provide more accurate information for planning purposes.

Identifying and mapping crops provides important information to agricultural lands management and cultivation area estimation of crops. Optical and radar images are valuable resources for classifiying agricultural land. Features deriverd from optical images contain information about the reflectance signatures of various products, while radar images provides information about the structural characteristics and distribution mechanisms of products. The combination of these two sources can create a complementary dataset with a significant number of spectral, texture and polarized temporal features for the classification of agricultural land

This study aims to explore the significance of red edge bands for the segregation of crops such aswheat, barley, alfalfa, beans, broad beans, flax, corn, sugar beet and potatoes using the random forest method and support vector machine. To conduct the analysis, a time series of Sentinel-1 and 2 images 2019 in the northwest region of Ardabil was retrieved from the Google Earth Engine (GEE) platform. The study evaluates the effectiveness of spectral and temporal information, plant indices and backscatter information on the crop mapping by examining different combinations of bands. Through the random forest feature selection method, essential features are identified and utilized as inputs for both the random forest and support vector machine classifiers.

The random forest provided the most favorable outcomes across all scenarios. The results revealed that incorporating red edge wavelengths and red edge-based vegetation indices proved more beneficial than other bands and vegetation indices for differentiating between barley, beans, broad beans, and flax. The most optimal outcome among various feature combinations was associated with the time series of spectral features from Sentinel-2 images combined with the time series of Sentinel-1 images, resulting in an overall accuracy of 84.67% and a kappa coefficient of 82.31%.  Furthermore, the results demonstrated that red edge bands and red edge-based vegetation indices effectively distinguish between different types of crops

It is recommended to carefully consider the selection of specific spectral bands to achieve higher accuracy in separation of crops. It is important to highlight that combining radar and optical images consistently yields superior results compared to classification methods based on a single sensor, leading to increased classification information.

The moisture content of sugarcane sheath is a crucial parameter during the crop's growth period, as it plays a key role in understanding water stress and field irrigation management. Traditional methods of measuring crop moisture levels involve time-consuming and expensive processes like obtaining wet and dry weights, followed by calculating moisture content, which are impractical for large areas. Recent advancements in remote sensing technology have enabled the monitoring of plant tissue moisture content in large fields. Remote sensing data have a high capacity to update crop growth monitoring systems.  In this regard, it is possible to use satellite images that provide a wealth of information to users. This research aims to evaluate sugarcane leaf sheath moisture using satellite images and generate moisture maps based on the best model.

The sugarcane fields, which represent the largest agricultural operations in Khuzestan, have an area of over 84,000 hectares. It covers over 9,670 hectares are cultivated by the Amir Kabir Agriculture and Industry company, the focus of this research. The study area is located at a latitude of 31° 00' 20' N and a longitude of 48° 15' 22' E. A total of 18 farms of the sugarcane variety CP69-1062 were utilized for this research. Five points were selected from each farm, and the coordinates of the points were recorded using a GPS device. The study was carried out between July and September. Ground data were collected nearly simultaneously with the Sentinel-2 satellite imaging of the target area. The moisture content of each collected sample was determined gravimetrically in the laboratory. For each image, indices and spectral bands were calculated using QGIS software and the output was saved as Excel and TIF files. The indices and bands obtained from Sentinel-2 satellite images were used to estimate and monitor the moisture status of sugarcane leaf sheath. In the next step, a variance inflation factor (VIF) analysis was implemented to check the collinearity between indices and bands. Finally, the indices of NDVI, EVI, SRWI, Clgreen and single bands B2, B3, B4, B5, B6, B11 and B12 were entered as input to four GRNN, RF, SVR and PLSR models. The Bayes algorithm was employed to optimize the parameters of the model.

The results demonstrated that the SVR model exhibited a superior ability to estimate leaf sheath moisture compared to other models. Additionally, the sensitivity analysis revealed that the SRWI, Clgreen, NDVI, B5, B12, B11, B4, B3, EVI and B2 parameters are effective parameters in the moisture content modelling process. In the final stage, the leaf sheath moisture was classified into five stress classes, namely irrigation time, low moisture, medium moisture, and high moisture, in the order from low to high. The results of the moisture maps and the irrigation schedule for each date indicate that the combined output of B2, B3, B4, B5, B6, B11, B12, NDVI, EVI, SRWI and Clgreen indices and bands has a superior performance. These indices were utilized in the preparation of irrigation plans. This method was employed to assess the potential of S2 MSI spectral indices for the estimation of leaf sheath moisture in the sugarcane growth stage.

Based on sensitivity analysis, the SRWI parameter was found to be the most effective index in the modelling process. Consequently, it can be concluded that a combination of indices and bands of NDVI, EVI, SRWI, Clgreen, B2, B3, B5, B4, B11, and B12 provides a more accurate estimate of sugarcane sheath moisture than any single input. Thus, processing and analysis of Sentinel-2 satellite images can be used to enhance the methodologies employed for the monitoring of sugarcane sheath moisture content in expansive fields.

Dengue fever is Chickenpox is a contagious and viral disease that is transmitted by two species of Aedes Egyptian mosquitoes and Aedes albopicus mosquitoes and is spreading rapidly in the world. The main purpose of this study is to model the spatial distribution of carriers of this disease in Iran. Due to the lack of sufficient carrier data in the country, carrier data available worldwide and also in Asia, in two different scales were used. Among the most important aspects of this research, we can name the use of heterogeneous layer as an auxiliary factor to analyze the presence points and reduce spatial autocorrelation and the use and comparison of two species distribution models based on presence data for Selecting the optimal modeling method. In this regard, first, using the maximum entropy method (MaxEnt) and a type of genetic algorithm called GARP (GARP), the level of habitat suitability in the world with a spatial resolution of 5 km for both species was modeled. To evaluate the mentioned models, the variables of population density, climate, vegetation density, altitude and soil organic carbon were considered. Due to the high accuracy of the MaxEnt method, using this method, the habitat suitability of the Asian continent with a resolution of 900 m was modeled for both species. The values under the curve (AUC) for the two types of carriers were calculated to be 0.9. The results showed that the northern and southern provinces of the country have higher habitat suitability for both species, with the difference that Aedes aegypti species in the southern to eastern parts of the Oman Sea coast has a higher probability of distribution. In implementing the MaxEnt method for Aedes albopicus, the provinces in western Iran were also identified as desirable, which was not properly modeled on a smaller scale. In February 2016, unfortunately, a small number of mosquitoes and AIDS mosquito eggs were discovered in Bandar Lengeh, which was exactly what this research had predicted. The results of this study can be used in line with planning for population management of these vector insects to control the disease at the same time as monitoring populations during epidemic seasons.

Hyperspectral remote sensing technology has witnessed remarkable progress in the last two decades. One of the analyzes performed on the hyperspectral images is target detection. In this research, the detection of roofs with special cover has been done as a target in an urban environment. Simultaneously with the growth of urbanization and the development of urban areas, the need of managers and planners for very accurate maps of urban areas has increased significantly. Since an urban environment has complex characteristics in terms of physical, geometrical and elements used in buildings, hyperspectral data effectively help to identify, extract and produce a map of the constituent elements of an urban environment. Regarding the spectral detection of the target, continuous and numerous researches have been carried out since the last two decades. According to the studies carried out, until now, the hierarchical algorithm has achieved the best results in comparison with other algorithms for extracting spatial information in hyperspectral images, Therefore, in this research, it is tried to reveal buildings with special cover in hyperspectral images by presenting a new and accurate method.

The image data of the CASI sensor has been used to carry out this research. The images processed in this research include images with 32 spectral bands and a resolution of 2 meters, which were taken in May 2001 from the urban area of ​​Toulouse located in the south of France. In the proposed method, two classification algorithms of multilayer perceptron neural network (MLP) and support vector machine (SVM) are implemented on the hyperspectral image. Then, the map resulting from the combination of the two mentioned algorithms is used to select the marker for the marker-based hierarchical segmentation algorithm. Finally, with the help of the majority vote decision rule, the marker-based hierarchical segmentation map is combined with the map resulting from the integration of MLP and SVM classifications.

In this research, Gaussian radial basis kernel was used to implement the SVM algorithm. The values ​​of two parameters, penalty (C) and width of Gaussian function () were determined in SVM algorithm with the help of cross validation technique. The MLP classification algorithm was implemented with 3 hidden layers that include 5, 6 and 8 neurons and its evaluation was done with 500 repetitions and to select markers, the analysis of the labeling of connected components was done based on 8 neighborhood pixels on the map resulting from the combination of MLP and SVM. Based on the obtained results, the map obtained from the proposed method includes uniform regions and has more interconnected structures to reveal buildings, which shows the importance of using spatial information along with spectral information.

In this research, the strategy of using spatial information along with spectral information to improve target detection in the analysis of hyperspectral images was examined. For this purpose, the spectral-spatial marker-based hierarchical algorithm, which is used in the image classification process, was used to reveal the roofs of the buildings. In the proposed method, two classification maps were used in the selection of markers and the decision rule of the majority vote in the case of the initial hierarchical segmentation algorithm. In the combination of MLP and SVM classification maps, conditional probability and selection of the highest probability of each pixel belonging to a class are used in the selection of markers and majority vote decision rule.

According to scientific evidence, the concentration of large amounts of greenhouse gases in the atmosphere leads to an increase in temperature not only at the Earth's surface but also in the troposphere. Despite short-term fluctuations, evidence indicates ongoing global heat accumulation. At night, after sunset, the short rays are no longer present and the outgoing long rays determine the air temperature. Therefore, the nighttime temperature is particularly influenced by variations in greenhouse gases, including CO2. A portion of the Earth's long-wave radiation passes through the atmospheric windows, while the rest is sent back to the Earth's surface as long-wave radiation by greenhouse gases. This process is crucial for maintaining the Earth's temperature balance, particularly during nights and the winter season. In this study, the AIRS sensor images were utilized for the first time to analyze nighttime temperature and carbon dioxide levels in Iran. Research and studies in this regard help managers and planners to raise awareness and promote climate policies to reduce and prevent possible risks caused by changes in the amount of carbon dioxide emissions and, as a result, climate change.

To investigate the changes in CO2 and night temperature and to assess the effect of carbon dioxide on the night temperature variable from 2003 to 2016 (January, May, July and November), monthly and seasonal time series graphs were examined. The Mann-Kendall test was used to assess the significance or non-significance of the trends and the type of trend. Pearson's correlation test and inverse non-linear regression were used for statistical analysis of night temperature and CO2 data. Night temperature maps were produced and classified using the IDW interpolation method.

Statistical analysis of the average night temperature distribution in Iran showed that the average temperature at the end of the study period (2016) was 0.42 o K higher than the average for the entire study period. The highest fluctuations in night temperature were associated with the winter season and the months of November and January, and the lowest fluctuations were associated with the summer season and July. The seasonal average trend of CO2 was completely increasing in all seasons and showed little fluctuation. Based on the distribution of the seasonal mean night temperature, the minimum night temperature was observed in the Northwest, Alborz, Zagros, and North Khorasan regions in all seasons. The southern coasts and southern coastal cities from Khuzestan province to Sistan and Baluchistan province, as well as the Kavir and Lut deserts, have the maximum night temperature in all seasons. Based on the average night temperature maps from 2003, 2006, 2009, 2012, 2015, and 2016, the spatial distribution of night temperatures in Iran shows an increase in January in certain central areas. These areas include parts of Semnan, Yazd, Isfahan, South Khorasan, and southeastern Iran. According to the results of the Mann-Kendall test, the trend of night temperature in July was upward and significant, and the trend of CO2 was upward and significant in all months. According to the results of Pearson's test, the night temperature in July has a high correlation (0.66) with CO2. The results of the non-linear regression model between the two variables showed that, with a coefficient of determination of 0.44, the maximum CO2 had the greatest influence on the maximum July night temperature during the study period.

According to the results of this research, the relationship between the rising trend of night temperature and carbon dioxide in July has been confirmed. Thus, the possibility of increasing night temperatures due to increasing carbon dioxide emissions in different regions of the world can be the subject of research by scientists. It should also be noted that in long-term trends there are sometimes periodic changes that last more than a year. If another study and research related to the subject of the present research is conducted and a longer time series (several decades) is studied, perhaps the dominant trend in Iran will show different conditions.

The 63.20 kV substations serve as the energy injection points from the transmission network to the distribution network. Determining the substation's location is important from both technical and economic perspectives for regional electricity companies and electricity distribution companies. This research aims to identify the optimal location for constructing a 63.20 kV substation, distinguishing its methodology from similar studies by considering criteria specific to electricity distribution companies and the internal interactions and influences of these criteria.

This study employs the network analysis process (ANP) and fuzzy logic within Geographic Information System (GIS). Based on expert evaluations, 13 criteria were identified as primary factors for substation location. ArcGIS software was utilized to create criteria layers. To standardize the criteria layers, data were normalized between 0 and 1 using fuzzy membership functions. The ANP method was then used to determine the final weightings of the criteria, which were applied to their corresponding fuzzy maps. For the final analysis, the gamma operator with the gamma values of 0.7, 0.8, and 0.9 was employed. Optimal gamma value selection was implemented using the gamma correlation coefficient test in SPSS software, leading to the calculation of the correlation coefficient and standard deviation.

The standard deviation calculations indicated that a gamma value of 0.7 has higher accuracy compared to other values. In the final map, areas around the city of Kahak were identified as the optimal location for constructing the 63.20 kV substation in Qom province

Land is a vital natural resource for human survival and the basis of all ecosystem services. However, land degradation through land use and land cover changes has become a serious global issue. Rural-urban migration in developing countries, along with the conversion of nearby villages into urban areas, has largely driven the rapid increase in urban settlements. Evidence shows that northern Iran has been experiencing rapid land use changes, which have endangered the region's stability over the last three decades. This article aims to identify and compare the growth and expansion changes of Amol city, located in Mazandaran province, over three decades from 1986 to 2016. Urban growth is defined by the absolute increase in the population living in cities, while urban sprawl is defined by changes in land cover over time.

To produce images of land cover changes in different years, Landsat time series image data (TM, ETM+, and OLI/TIRS sensors) were used. A linear unmixing model based on the extraction of end members was employed, identifying two classes of land: urban and non-urban. To verify the classification accuracy, high-resolution images from Google Earth and field observations using GPS were employed. Moran's spatial autocorrelation analysis tool was used to analyze the spatial distribution of land cover, and the trend analysis tool in ArcGIS software was employed to analyze the trend of spatial changes in urban land.

The findings show that the city's population doubling over these three decades has not only been accompanied by a 60% horizontal expansion into surrounding green and open lands but also increased building density and the number of floors. Validation of land cover classification, using the ambiguity error matrix, with an overall accuracy of over 90% and a kappa coefficient of over 80% for the obtained images, indicates high accuracy. Based on Moran's spatial autocorrelation analysis, the development of Amol City follows a cluster structure. Calculating the difference in land cover data over the 20 years from 1996 to 2016 revealed that 28 percent of the total city area, equivalent to 890 hectares, has undergone significant changes, including fundamental alterations in land use and land cover. The expansion of Amol City has generally occurred from the center to the periphery, with the least changes in the central parts and the most changes in the northern and eastern parts of the city.

Population growth and urban expansion not only lead to the destruction of natural resources and the environment but also exacerbate urban management problems in providing infrastructure and services. Therefore, the goal of planning should be to achieve optimal development that prevents increased migration from villages to cities. This can be achieved both at the macro level, by distributing urban roles complementarily and strengthening the role of villages, and at the local level, by emphasizing the balanced distribution of the city's population and endogenous growth.