فهرست مطالب hossein shafizadeh-moghadam

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.

One of the inevitable consequences of the ever-increasing growth of the world’s population is the expansion of urbanization. So, it is very important to provide a vision of the spatial development of cities with the aim of understanding the correct pattern of city growth and providing the necessary infrastructure. Since Qom metropolis faced urban growth and has recorded 95% urbanization, this research focused on the spatial development of urban lands around this metropolis. First, the land use/cover and the urban growth merit maps were produced. Land use/ land cover maps of the region for the years 2000, 2010 and 2020 were produced using the random forest method in the Google Earth Engine, and the urban growth merit map for the years 2000 and 2010 was produced using MCDM analyses based on GIS. Finally, based on the ANN-CA-Markov and SVM-CA-Markov algorithms, 2020’s land use/cover maps were simulated. The validation of the models showed that the SVM-CA-Markov algorithm with the ROC (0.96) was more accurate and was chosen as the optimal algorithm for modeling the horizon of 2040. The results indicate the increasing spatial development of this metropolis. The area of urban land in this region will increase from 139.62 square kilometers in 2020 to more than 183 square kilometers in 2040. The evaluation of the results can help the relevant managers in order to take necessary policies to manage the situation as best as possible. This importance can be realized through planning for the regular development of the road network, the expansion of urban green spaces, etc. official organizations and local officials should have a purposeful monitoring of this issue, while having full control over the development directions of this metropolis.

The climate of an area over a long period is called the climate. Climate identification and classification have long been of interest to meteorologists. Researchers have classified the earth into homogeneous climatic zones using different methods and climatic variables such as rainfall and temperature. They have used the results of climate zoning to assess water scarcity and water resources on a small and large scale to anticipate practical measures to control drought in vulnerable areas. The purpose of this study is to update and analyze the Spatio-temporal analysis of Iran's climatic classification based on the Domarten index and the Mann-Kendall test. Because in studies based on climate classification maps, up-to-date maps can better help understand the study area.

For this study, data related to temperature and precipitation variables were extracted monthly from 153 synoptic stations from 1995-2019 from the Meteorological Organization of Iran. First, the data of average annual temperature and total annual precipitation were obtained from monthly data and then, using isothermal and isothermal maps, were obtained using the kriging model. The final climate zoning map was prepared using the De-Martonne index based on rainfall and temperature. The non-parametric Mann-Kendall test was also used to evaluate the significance or non-significance of the De-Martonne climate index and determine the trend.

The results showed that based on the Kriging model, R2 and RMSE for precipitation data were 0.58 and 167.51 mm, respectively, and for the data were 0.83 and 2.23 ° C, respectively. This indicates better performance of the model for temperature data. This is related to the high variance of precipitation data in the country. Iran's climatic zoning based on the De-Martonne index showed six main climatic types in Iran. Most of the area of Iran is an arid climate and then a semi-arid climate. The study results showed that arid climate is 76.40%, the semi-arid climate is 19.65%, and other climates make up less than 4% of the area of Iran. Also, the area of arid and semi-arid climates with an area of 96.05% of the area of Iran has increased compared to previous research, which may be due to reduced rainfall and increased temperature. Also, the results of the Mann-Kendall test showed that Khorramdareh, Miyaneh, Ramsar, Boroujerd, Piranshahr, Tabriz, and Bijar stations have a significant upward trend (wetting trend), and Dezful, Malayer, Sabzevar, Bandar Anzali, Tehran (Mehrabad), Tehran (Shemiran), Qazvin and Dushan Tappeh stations have a significant downward trend (drying trend) at the level of 5%.

This study showed that Iran has six climatic regions, including; The climate is arid, semi-arid, Mediterranean, semi-humid, humid, and very humid. Also, comparing the results with the results of research done by other researchers in the past showed that the area of arid and semi-arid climates in the study period has increased compared to previous periods. Also, 14% of stations with a downtrend (8 stations) have a significant downtrend, and 7% of stations with an uptrend (7 stations) have a significant uptrend at 95%.

Karkheh is one of the most important watersheds for water resources management and croplands in Iran, where the largest dam in Iran and the Middle East is located there. Karkheh is considered as Iran’s food basket and exploring land-use changes in this watershed has highly strategic. In the present study, land-use changes during 1990 and 2020 in the Karkheh basin have been extracted and evaluated using Landsat satellite images and random forest algorithm in the Google Earth Engine platform. In this paper, the changes of 11 classes, including forest, shrubland, grassland, irrigated, rainfed, garden, barren, water body, wetland, urban, and riparian have been quantified. The largest area of the region was belong to grassland and barren. In this research, the classification process has been done separately for each Landsat image scene in the Karkheh basin, and finally, all the scenes have been mosaic together. Using this method, most of the images in a scene are used, and the time series of indexes specific to each class of each scene is used for classification, which achieves more accurate results than the method of classifying the whole area in one place. The results show urban areas have increased by 113%, water bodies by 149%, garden by 163%, riparian by 39%, irrigated by 122%, wetland by 10% and rainfed by 34%. However, forest 22%, barren 20%, and shrubs 20% were reduced. As a result, this statistic indicates an expansion of agriculture and reduction of grassland. The accuracy assessment of the classified images confirmed the overall accuracy and kappa coefficient as being 96% and 95% for 1990, 94%, and 93% for 2020. These indices show the appropriate accuracy of classification maps and the validity of the results.

 Gully erosion is a subtype of water erosion which makes agricultural lands impracticable during its development. Given the geographical and environmental conditions, various factors contribute to the development and expansion of gully erosion. In this study, due to the extensive expansion of gully erosion in Jafarabad Moghan, and damaging the agricultural and rangelands, the probability of gully occurrence and the spatial effects of its drivers has been investigated.

 In this study, using a boosted regression tree model, the effect of the following factors on the gully occurrence were investigated; slope, aspect, plan curvature, altitude, clay content of horizon A, clay content of horizon B, sand content of horizon A, sand content of horizon B, surface organic matter, distance from roads and distance from rivers. Then, the susceptibility map of the gully erosion was created.

 The results showed that the distance from river, the percentage of sand of horizon A, the percentage of clay of horizon A, and the surface organic matter with 16.3%, 13.1%, 11.4% and 10.7% respectively, were the most important influential factors of gully occurrence. On the other hand, aspect (4.5%) and elevation (5.5%) were the least important ones which could be due to the lack of significant elevation shift in the region. Based on the susceptibility map, 10/63% of the study area was classified as very sensitive to the gully erosion. The AUC value for the boosted tree regression model was 0.81 which indicates a good model performance in the prediction of areas sensitive to gully erosion.

 The results of this study shows the critical influence of surface soil properties on the gully erosion. Considering the fact that the greatest effect on the probability of gully erosion is related to distance from the river and surface soil properties, it is possible to manage the lands susceptible to gully erosion by effective management of streams and existing gullies, and also by training the farmers and increasing their knowledge regarding gully erosion, land management and sustainable agriculture. The results indicate the suitability of the boosted regression trees for the similar studies.